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| 44f297e55a |
2
.gitignore
vendored
2
.gitignore
vendored
@@ -1,4 +1,4 @@
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data
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data
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build
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build
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out.txt
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out.txt
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result.csv
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*.csv
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22
README.md
22
README.md
@@ -10,10 +10,30 @@
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|||||||
не менее чем на 10% от даты начала интервала, вместе с минимальными и максимальными
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не менее чем на 10% от даты начала интервала, вместе с минимальными и максимальными
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||||||
значениями Open и Close за все дни внутри интервала.
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значениями Open и Close за все дни внутри интервала.
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||||||
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||||||
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## Параллельное чтение данных
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||||||
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||||||
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Нет смысла параллельно читать данные из NFS, так как в реальности файлы с данными
|
||||||
|
будут лежать только на NFS сервере. То есть другие узлы лишь отправляют сетевые запросы
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||||||
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на NFS сервер, который уже читает реальные данные с диска и лишь затем отправляет
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||||||
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их другим узлам.
|
||||||
|
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||||||
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Чтобы этого избежать, нужно на всех машинах скопировать файлы с данными в их реальные
|
||||||
|
файловые системы. Например в папку `/data`.
|
||||||
|
|
||||||
|
```sh
|
||||||
|
# На каждом узле создаем директорию /data
|
||||||
|
sudo mkdir /data
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||||||
|
sudo chown $USER /data
|
||||||
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||||||
|
# Копируем данные
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||||||
|
cd /mnt/shared/supercomputers/data
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||||||
|
cp data.csv /data/
|
||||||
|
```
|
||||||
|
|
||||||
## Сборка
|
## Сборка
|
||||||
|
|
||||||
Проект обязательно должен быть расположен в общей директории для всех узлов,
|
Проект обязательно должен быть расположен в общей директории для всех узлов,
|
||||||
например, в `/mnt/shared/supercomputers/bitcoin-project/build`.
|
например, в `/mnt/shared/supercomputers/build`.
|
||||||
Перед запуском указать актуальный путь в `run.slurm`.
|
Перед запуском указать актуальный путь в `run.slurm`.
|
||||||
|
|
||||||
```sh
|
```sh
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||||||
|
|||||||
445
data.ipynb
445
data.ipynb
@@ -2,7 +2,7 @@
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|||||||
"cells": [
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"cells": [
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||||||
{
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{
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||||||
"cell_type": "code",
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"cell_type": "code",
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||||||
"execution_count": 1,
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"execution_count": 20,
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||||||
"id": "2acce44b",
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"id": "2acce44b",
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||||||
"metadata": {},
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"metadata": {},
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||||||
"outputs": [],
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"outputs": [],
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||||||
@@ -12,7 +12,7 @@
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|||||||
},
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},
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||||||
{
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{
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||||||
"cell_type": "code",
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"cell_type": "code",
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||||||
"execution_count": 14,
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"execution_count": 21,
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||||||
"id": "5ba70af7",
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"id": "5ba70af7",
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||||||
"metadata": {},
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"metadata": {},
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||||||
"outputs": [
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"outputs": [
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||||||
@@ -111,7 +111,7 @@
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|||||||
"7317758 0.410369 "
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"7317758 0.410369 "
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||||||
]
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]
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||||||
},
|
},
|
||||||
"execution_count": 14,
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"execution_count": 21,
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||||||
"metadata": {},
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"metadata": {},
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||||||
"output_type": "execute_result"
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"output_type": "execute_result"
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||||||
}
|
}
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||||||
@@ -124,8 +124,8 @@
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|||||||
},
|
},
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||||||
{
|
{
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||||||
"cell_type": "code",
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"cell_type": "code",
|
||||||
"execution_count": 19,
|
"execution_count": 23,
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||||||
"id": "d4b22f3b",
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"id": "3b320537",
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||||||
"metadata": {},
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"metadata": {},
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||||||
"outputs": [
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"outputs": [
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||||||
{
|
{
|
||||||
@@ -150,67 +150,190 @@
|
|||||||
" <tr style=\"text-align: right;\">\n",
|
" <tr style=\"text-align: right;\">\n",
|
||||||
" <th></th>\n",
|
" <th></th>\n",
|
||||||
" <th>Timestamp</th>\n",
|
" <th>Timestamp</th>\n",
|
||||||
" <th>Low</th>\n",
|
|
||||||
" <th>High</th>\n",
|
|
||||||
" <th>Open</th>\n",
|
" <th>Open</th>\n",
|
||||||
|
" <th>High</th>\n",
|
||||||
|
" <th>Low</th>\n",
|
||||||
" <th>Close</th>\n",
|
" <th>Close</th>\n",
|
||||||
|
" <th>Volume</th>\n",
|
||||||
|
" <th>Avg</th>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" </thead>\n",
|
" </thead>\n",
|
||||||
" <tbody>\n",
|
" <tbody>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>5078</th>\n",
|
" <th>7317754</th>\n",
|
||||||
" <td>2025-11-26</td>\n",
|
" <td>2025-11-30 23:55:00+00:00</td>\n",
|
||||||
" <td>86304.0</td>\n",
|
" <td>90405.0</td>\n",
|
||||||
" <td>90646.0</td>\n",
|
" <td>90452.0</td>\n",
|
||||||
" <td>87331.0</td>\n",
|
" <td>90403.0</td>\n",
|
||||||
" <td>90477.0</td>\n",
|
" <td>90452.0</td>\n",
|
||||||
|
" <td>0.531700</td>\n",
|
||||||
|
" <td>90427.5</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>5079</th>\n",
|
" <th>7317755</th>\n",
|
||||||
" <td>2025-11-27</td>\n",
|
" <td>2025-11-30 23:56:00+00:00</td>\n",
|
||||||
" <td>90091.0</td>\n",
|
" <td>90452.0</td>\n",
|
||||||
" <td>91926.0</td>\n",
|
" <td>90481.0</td>\n",
|
||||||
" <td>90476.0</td>\n",
|
" <td>90420.0</td>\n",
|
||||||
" <td>91325.0</td>\n",
|
" <td>90420.0</td>\n",
|
||||||
|
" <td>0.055547</td>\n",
|
||||||
|
" <td>90450.5</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>5080</th>\n",
|
" <th>7317756</th>\n",
|
||||||
" <td>2025-11-28</td>\n",
|
" <td>2025-11-30 23:57:00+00:00</td>\n",
|
||||||
" <td>90233.0</td>\n",
|
" <td>90412.0</td>\n",
|
||||||
" <td>93091.0</td>\n",
|
" <td>90458.0</td>\n",
|
||||||
" <td>91326.0</td>\n",
|
" <td>90396.0</td>\n",
|
||||||
" <td>90913.0</td>\n",
|
" <td>90435.0</td>\n",
|
||||||
|
" <td>0.301931</td>\n",
|
||||||
|
" <td>90427.0</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>5081</th>\n",
|
" <th>7317757</th>\n",
|
||||||
" <td>2025-11-29</td>\n",
|
" <td>2025-11-30 23:58:00+00:00</td>\n",
|
||||||
" <td>90216.0</td>\n",
|
" <td>90428.0</td>\n",
|
||||||
" <td>91179.0</td>\n",
|
" <td>90428.0</td>\n",
|
||||||
" <td>90913.0</td>\n",
|
" <td>90362.0</td>\n",
|
||||||
" <td>90832.0</td>\n",
|
" <td>90362.0</td>\n",
|
||||||
" </tr>\n",
|
" <td>4.591653</td>\n",
|
||||||
" <tr>\n",
|
" <td>90395.0</td>\n",
|
||||||
" <th>5082</th>\n",
|
" </tr>\n",
|
||||||
" <td>2025-11-30</td>\n",
|
" <tr>\n",
|
||||||
|
" <th>7317758</th>\n",
|
||||||
|
" <td>2025-11-30 23:59:00+00:00</td>\n",
|
||||||
|
" <td>90363.0</td>\n",
|
||||||
|
" <td>90386.0</td>\n",
|
||||||
" <td>90362.0</td>\n",
|
" <td>90362.0</td>\n",
|
||||||
" <td>91969.0</td>\n",
|
|
||||||
" <td>90832.0</td>\n",
|
|
||||||
" <td>90382.0</td>\n",
|
" <td>90382.0</td>\n",
|
||||||
|
" <td>0.410369</td>\n",
|
||||||
|
" <td>90374.0</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" </tbody>\n",
|
" </tbody>\n",
|
||||||
"</table>\n",
|
"</table>\n",
|
||||||
"</div>"
|
"</div>"
|
||||||
],
|
],
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
" Timestamp Low High Open Close\n",
|
" Timestamp Open High Low Close \\\n",
|
||||||
"5078 2025-11-26 86304.0 90646.0 87331.0 90477.0\n",
|
"7317754 2025-11-30 23:55:00+00:00 90405.0 90452.0 90403.0 90452.0 \n",
|
||||||
"5079 2025-11-27 90091.0 91926.0 90476.0 91325.0\n",
|
"7317755 2025-11-30 23:56:00+00:00 90452.0 90481.0 90420.0 90420.0 \n",
|
||||||
"5080 2025-11-28 90233.0 93091.0 91326.0 90913.0\n",
|
"7317756 2025-11-30 23:57:00+00:00 90412.0 90458.0 90396.0 90435.0 \n",
|
||||||
"5081 2025-11-29 90216.0 91179.0 90913.0 90832.0\n",
|
"7317757 2025-11-30 23:58:00+00:00 90428.0 90428.0 90362.0 90362.0 \n",
|
||||||
"5082 2025-11-30 90362.0 91969.0 90832.0 90382.0"
|
"7317758 2025-11-30 23:59:00+00:00 90363.0 90386.0 90362.0 90382.0 \n",
|
||||||
|
"\n",
|
||||||
|
" Volume Avg \n",
|
||||||
|
"7317754 0.531700 90427.5 \n",
|
||||||
|
"7317755 0.055547 90450.5 \n",
|
||||||
|
"7317756 0.301931 90427.0 \n",
|
||||||
|
"7317757 4.591653 90395.0 \n",
|
||||||
|
"7317758 0.410369 90374.0 "
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 19,
|
"execution_count": 23,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"df['Avg'] = (df['Low'] + df['High']) / 2\n",
|
||||||
|
"df.tail()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 25,
|
||||||
|
"id": "4b1cd63c",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Timestamp</th>\n",
|
||||||
|
" <th>Avg</th>\n",
|
||||||
|
" <th>OpenMin</th>\n",
|
||||||
|
" <th>OpenMax</th>\n",
|
||||||
|
" <th>CloseMin</th>\n",
|
||||||
|
" <th>CloseMax</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5078</th>\n",
|
||||||
|
" <td>2025-11-26</td>\n",
|
||||||
|
" <td>88057.301736</td>\n",
|
||||||
|
" <td>86312.0</td>\n",
|
||||||
|
" <td>90574.0</td>\n",
|
||||||
|
" <td>86323.0</td>\n",
|
||||||
|
" <td>90574.0</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5079</th>\n",
|
||||||
|
" <td>2025-11-27</td>\n",
|
||||||
|
" <td>91245.092708</td>\n",
|
||||||
|
" <td>90126.0</td>\n",
|
||||||
|
" <td>91888.0</td>\n",
|
||||||
|
" <td>90126.0</td>\n",
|
||||||
|
" <td>91925.0</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5080</th>\n",
|
||||||
|
" <td>2025-11-28</td>\n",
|
||||||
|
" <td>91324.308681</td>\n",
|
||||||
|
" <td>90255.0</td>\n",
|
||||||
|
" <td>92970.0</td>\n",
|
||||||
|
" <td>90283.0</td>\n",
|
||||||
|
" <td>92966.0</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5081</th>\n",
|
||||||
|
" <td>2025-11-29</td>\n",
|
||||||
|
" <td>90746.479514</td>\n",
|
||||||
|
" <td>90265.0</td>\n",
|
||||||
|
" <td>91158.0</td>\n",
|
||||||
|
" <td>90279.0</td>\n",
|
||||||
|
" <td>91179.0</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>5082</th>\n",
|
||||||
|
" <td>2025-11-30</td>\n",
|
||||||
|
" <td>91187.356250</td>\n",
|
||||||
|
" <td>90363.0</td>\n",
|
||||||
|
" <td>91940.0</td>\n",
|
||||||
|
" <td>90362.0</td>\n",
|
||||||
|
" <td>91940.0</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Timestamp Avg OpenMin OpenMax CloseMin CloseMax\n",
|
||||||
|
"5078 2025-11-26 88057.301736 86312.0 90574.0 86323.0 90574.0\n",
|
||||||
|
"5079 2025-11-27 91245.092708 90126.0 91888.0 90126.0 91925.0\n",
|
||||||
|
"5080 2025-11-28 91324.308681 90255.0 92970.0 90283.0 92966.0\n",
|
||||||
|
"5081 2025-11-29 90746.479514 90265.0 91158.0 90279.0 91179.0\n",
|
||||||
|
"5082 2025-11-30 91187.356250 90363.0 91940.0 90362.0 91940.0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 25,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@@ -218,7 +341,13 @@
|
|||||||
"source": [
|
"source": [
|
||||||
"df_days = (\n",
|
"df_days = (\n",
|
||||||
" df.groupby(df[\"Timestamp\"].dt.date)\n",
|
" df.groupby(df[\"Timestamp\"].dt.date)\n",
|
||||||
" .agg({\"Low\": \"min\", \"High\": \"max\", \"Open\": \"first\", \"Close\": \"last\"})\n",
|
" .agg(\n",
|
||||||
|
" Avg=(\"Avg\", \"mean\"),\n",
|
||||||
|
" OpenMin=(\"Open\", \"min\"),\n",
|
||||||
|
" OpenMax=(\"Open\", \"max\"),\n",
|
||||||
|
" CloseMin=(\"Close\", \"min\"),\n",
|
||||||
|
" CloseMax=(\"Close\", \"max\"),\n",
|
||||||
|
" )\n",
|
||||||
" .reset_index()\n",
|
" .reset_index()\n",
|
||||||
")\n",
|
")\n",
|
||||||
"df_days.tail()"
|
"df_days.tail()"
|
||||||
@@ -226,111 +355,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 21,
|
"execution_count": 26,
|
||||||
"id": "91823496",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/html": [
|
|
||||||
"<div>\n",
|
|
||||||
"<style scoped>\n",
|
|
||||||
" .dataframe tbody tr th:only-of-type {\n",
|
|
||||||
" vertical-align: middle;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .dataframe tbody tr th {\n",
|
|
||||||
" vertical-align: top;\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
" .dataframe thead th {\n",
|
|
||||||
" text-align: right;\n",
|
|
||||||
" }\n",
|
|
||||||
"</style>\n",
|
|
||||||
"<table border=\"1\" class=\"dataframe\">\n",
|
|
||||||
" <thead>\n",
|
|
||||||
" <tr style=\"text-align: right;\">\n",
|
|
||||||
" <th></th>\n",
|
|
||||||
" <th>Timestamp</th>\n",
|
|
||||||
" <th>Low</th>\n",
|
|
||||||
" <th>High</th>\n",
|
|
||||||
" <th>Open</th>\n",
|
|
||||||
" <th>Close</th>\n",
|
|
||||||
" <th>Avg</th>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" </thead>\n",
|
|
||||||
" <tbody>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5078</th>\n",
|
|
||||||
" <td>2025-11-26</td>\n",
|
|
||||||
" <td>86304.0</td>\n",
|
|
||||||
" <td>90646.0</td>\n",
|
|
||||||
" <td>87331.0</td>\n",
|
|
||||||
" <td>90477.0</td>\n",
|
|
||||||
" <td>88475.0</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5079</th>\n",
|
|
||||||
" <td>2025-11-27</td>\n",
|
|
||||||
" <td>90091.0</td>\n",
|
|
||||||
" <td>91926.0</td>\n",
|
|
||||||
" <td>90476.0</td>\n",
|
|
||||||
" <td>91325.0</td>\n",
|
|
||||||
" <td>91008.5</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5080</th>\n",
|
|
||||||
" <td>2025-11-28</td>\n",
|
|
||||||
" <td>90233.0</td>\n",
|
|
||||||
" <td>93091.0</td>\n",
|
|
||||||
" <td>91326.0</td>\n",
|
|
||||||
" <td>90913.0</td>\n",
|
|
||||||
" <td>91662.0</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5081</th>\n",
|
|
||||||
" <td>2025-11-29</td>\n",
|
|
||||||
" <td>90216.0</td>\n",
|
|
||||||
" <td>91179.0</td>\n",
|
|
||||||
" <td>90913.0</td>\n",
|
|
||||||
" <td>90832.0</td>\n",
|
|
||||||
" <td>90697.5</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5082</th>\n",
|
|
||||||
" <td>2025-11-30</td>\n",
|
|
||||||
" <td>90362.0</td>\n",
|
|
||||||
" <td>91969.0</td>\n",
|
|
||||||
" <td>90832.0</td>\n",
|
|
||||||
" <td>90382.0</td>\n",
|
|
||||||
" <td>91165.5</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" </tbody>\n",
|
|
||||||
"</table>\n",
|
|
||||||
"</div>"
|
|
||||||
],
|
|
||||||
"text/plain": [
|
|
||||||
" Timestamp Low High Open Close Avg\n",
|
|
||||||
"5078 2025-11-26 86304.0 90646.0 87331.0 90477.0 88475.0\n",
|
|
||||||
"5079 2025-11-27 90091.0 91926.0 90476.0 91325.0 91008.5\n",
|
|
||||||
"5080 2025-11-28 90233.0 93091.0 91326.0 90913.0 91662.0\n",
|
|
||||||
"5081 2025-11-29 90216.0 91179.0 90913.0 90832.0 90697.5\n",
|
|
||||||
"5082 2025-11-30 90362.0 91969.0 90832.0 90382.0 91165.5"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 21,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"df_days[\"Avg\"] = (df_days[\"Low\"] + df_days[\"High\"]) / 2\n",
|
|
||||||
"df_days.tail()"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 25,
|
|
||||||
"id": "9a7b3310",
|
"id": "9a7b3310",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -358,6 +383,8 @@
|
|||||||
" <th>start_date</th>\n",
|
" <th>start_date</th>\n",
|
||||||
" <th>end_date</th>\n",
|
" <th>end_date</th>\n",
|
||||||
" <th>min_open</th>\n",
|
" <th>min_open</th>\n",
|
||||||
|
" <th>max_open</th>\n",
|
||||||
|
" <th>min_close</th>\n",
|
||||||
" <th>max_close</th>\n",
|
" <th>max_close</th>\n",
|
||||||
" <th>start_avg</th>\n",
|
" <th>start_avg</th>\n",
|
||||||
" <th>end_avg</th>\n",
|
" <th>end_avg</th>\n",
|
||||||
@@ -366,76 +393,86 @@
|
|||||||
" </thead>\n",
|
" </thead>\n",
|
||||||
" <tbody>\n",
|
" <tbody>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>335</th>\n",
|
" <th>316</th>\n",
|
||||||
" <td>2025-02-27</td>\n",
|
" <td>2025-02-27</td>\n",
|
||||||
" <td>2025-04-23</td>\n",
|
" <td>2025-04-25</td>\n",
|
||||||
" <td>76252.0</td>\n",
|
" <td>74509.0</td>\n",
|
||||||
" <td>94273.0</td>\n",
|
" <td>95801.0</td>\n",
|
||||||
" <td>84801.5</td>\n",
|
" <td>74515.0</td>\n",
|
||||||
" <td>93335.0</td>\n",
|
" <td>95800.0</td>\n",
|
||||||
" <td>0.100629</td>\n",
|
" <td>85166.063889</td>\n",
|
||||||
|
" <td>94303.907292</td>\n",
|
||||||
|
" <td>0.107294</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>336</th>\n",
|
" <th>317</th>\n",
|
||||||
" <td>2025-04-24</td>\n",
|
" <td>2025-04-26</td>\n",
|
||||||
" <td>2025-05-09</td>\n",
|
" <td>2025-05-11</td>\n",
|
||||||
" <td>93730.0</td>\n",
|
" <td>92877.0</td>\n",
|
||||||
" <td>103261.0</td>\n",
|
" <td>104971.0</td>\n",
|
||||||
" <td>92867.5</td>\n",
|
" <td>92872.0</td>\n",
|
||||||
" <td>103341.0</td>\n",
|
" <td>104965.0</td>\n",
|
||||||
" <td>0.112779</td>\n",
|
" <td>94500.950347</td>\n",
|
||||||
|
" <td>104182.167708</td>\n",
|
||||||
|
" <td>0.102446</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>337</th>\n",
|
" <th>318</th>\n",
|
||||||
" <td>2025-05-10</td>\n",
|
" <td>2025-05-12</td>\n",
|
||||||
" <td>2025-07-11</td>\n",
|
" <td>2025-07-11</td>\n",
|
||||||
" <td>100990.0</td>\n",
|
" <td>98384.0</td>\n",
|
||||||
" <td>117579.0</td>\n",
|
" <td>118833.0</td>\n",
|
||||||
" <td>103915.0</td>\n",
|
" <td>98382.0</td>\n",
|
||||||
" <td>117032.5</td>\n",
|
" <td>118839.0</td>\n",
|
||||||
" <td>0.126233</td>\n",
|
" <td>103569.791319</td>\n",
|
||||||
|
" <td>117463.666667</td>\n",
|
||||||
|
" <td>0.134150</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>338</th>\n",
|
" <th>319</th>\n",
|
||||||
" <td>2025-07-12</td>\n",
|
" <td>2025-07-12</td>\n",
|
||||||
" <td>2025-11-04</td>\n",
|
" <td>2025-11-04</td>\n",
|
||||||
" <td>106470.0</td>\n",
|
" <td>98944.0</td>\n",
|
||||||
" <td>124728.0</td>\n",
|
" <td>126202.0</td>\n",
|
||||||
" <td>117599.0</td>\n",
|
" <td>98943.0</td>\n",
|
||||||
" <td>103079.0</td>\n",
|
" <td>126202.0</td>\n",
|
||||||
" <td>0.123470</td>\n",
|
" <td>117640.026389</td>\n",
|
||||||
|
" <td>103712.985764</td>\n",
|
||||||
|
" <td>0.118387</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" <tr>\n",
|
" <tr>\n",
|
||||||
" <th>339</th>\n",
|
" <th>320</th>\n",
|
||||||
" <td>2025-11-05</td>\n",
|
" <td>2025-11-05</td>\n",
|
||||||
" <td>2025-11-18</td>\n",
|
" <td>2025-11-18</td>\n",
|
||||||
" <td>92112.0</td>\n",
|
" <td>89291.0</td>\n",
|
||||||
" <td>105972.0</td>\n",
|
" <td>107343.0</td>\n",
|
||||||
" <td>101737.5</td>\n",
|
" <td>89286.0</td>\n",
|
||||||
" <td>91471.0</td>\n",
|
" <td>107343.0</td>\n",
|
||||||
" <td>0.100912</td>\n",
|
" <td>102514.621181</td>\n",
|
||||||
|
" <td>91705.833333</td>\n",
|
||||||
|
" <td>0.105437</td>\n",
|
||||||
" </tr>\n",
|
" </tr>\n",
|
||||||
" </tbody>\n",
|
" </tbody>\n",
|
||||||
"</table>\n",
|
"</table>\n",
|
||||||
"</div>"
|
"</div>"
|
||||||
],
|
],
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
" start_date end_date min_open max_close start_avg end_avg \\\n",
|
" start_date end_date min_open max_open min_close max_close \\\n",
|
||||||
"335 2025-02-27 2025-04-23 76252.0 94273.0 84801.5 93335.0 \n",
|
"316 2025-02-27 2025-04-25 74509.0 95801.0 74515.0 95800.0 \n",
|
||||||
"336 2025-04-24 2025-05-09 93730.0 103261.0 92867.5 103341.0 \n",
|
"317 2025-04-26 2025-05-11 92877.0 104971.0 92872.0 104965.0 \n",
|
||||||
"337 2025-05-10 2025-07-11 100990.0 117579.0 103915.0 117032.5 \n",
|
"318 2025-05-12 2025-07-11 98384.0 118833.0 98382.0 118839.0 \n",
|
||||||
"338 2025-07-12 2025-11-04 106470.0 124728.0 117599.0 103079.0 \n",
|
"319 2025-07-12 2025-11-04 98944.0 126202.0 98943.0 126202.0 \n",
|
||||||
"339 2025-11-05 2025-11-18 92112.0 105972.0 101737.5 91471.0 \n",
|
"320 2025-11-05 2025-11-18 89291.0 107343.0 89286.0 107343.0 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" change \n",
|
" start_avg end_avg change \n",
|
||||||
"335 0.100629 \n",
|
"316 85166.063889 94303.907292 0.107294 \n",
|
||||||
"336 0.112779 \n",
|
"317 94500.950347 104182.167708 0.102446 \n",
|
||||||
"337 0.126233 \n",
|
"318 103569.791319 117463.666667 0.134150 \n",
|
||||||
"338 0.123470 \n",
|
"319 117640.026389 103712.985764 0.118387 \n",
|
||||||
"339 0.100912 "
|
"320 102514.621181 91705.833333 0.105437 "
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 25,
|
"execution_count": 26,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@@ -455,8 +492,10 @@
|
|||||||
" intervals.append({\n",
|
" intervals.append({\n",
|
||||||
" \"start_date\": df_days.loc[start_idx, \"Timestamp\"],\n",
|
" \"start_date\": df_days.loc[start_idx, \"Timestamp\"],\n",
|
||||||
" \"end_date\": df_days.loc[i, \"Timestamp\"],\n",
|
" \"end_date\": df_days.loc[i, \"Timestamp\"],\n",
|
||||||
" \"min_open\": interval[\"Open\"].min(),\n",
|
" \"min_open\": interval[\"OpenMin\"].min(),\n",
|
||||||
" \"max_close\": interval[\"Close\"].max(),\n",
|
" \"max_open\": interval[\"OpenMax\"].max(),\n",
|
||||||
|
" \"min_close\": interval[\"CloseMin\"].min(),\n",
|
||||||
|
" \"max_close\": interval[\"CloseMax\"].max(),\n",
|
||||||
" \"start_avg\": price_base,\n",
|
" \"start_avg\": price_base,\n",
|
||||||
" \"end_avg\": price_now,\n",
|
" \"end_avg\": price_now,\n",
|
||||||
" \"change\": change,\n",
|
" \"change\": change,\n",
|
||||||
@@ -470,6 +509,14 @@
|
|||||||
"df_intervals = pd.DataFrame(intervals)\n",
|
"df_intervals = pd.DataFrame(intervals)\n",
|
||||||
"df_intervals.tail()"
|
"df_intervals.tail()"
|
||||||
]
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "07f1cd58",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|||||||
10
run.slurm
10
run.slurm
@@ -5,8 +5,14 @@
|
|||||||
#SBATCH --cpus-per-task=2
|
#SBATCH --cpus-per-task=2
|
||||||
#SBATCH --output=out.txt
|
#SBATCH --output=out.txt
|
||||||
|
|
||||||
# Количество CPU потоков на узел (должно соответствовать cpus-per-task)
|
# Путь к файлу данных (должен существовать на всех узлах)
|
||||||
export NUM_CPU_THREADS=2
|
export DATA_PATH="/mnt/shared/supercomputers/data/data.csv"
|
||||||
|
|
||||||
|
# Доли данных для каждого ранка (сумма определяет пропорции)
|
||||||
|
export DATA_READ_SHARES="10,12,13,13"
|
||||||
|
|
||||||
|
# Размер перекрытия в байтах для обработки границ строк
|
||||||
|
export READ_OVERLAP_BYTES=131072
|
||||||
|
|
||||||
cd /mnt/shared/supercomputers/build
|
cd /mnt/shared/supercomputers/build
|
||||||
mpirun -np $SLURM_NTASKS ./bitcoin_app
|
mpirun -np $SLURM_NTASKS ./bitcoin_app
|
||||||
|
|||||||
@@ -1,87 +1,48 @@
|
|||||||
#include "aggregation.hpp"
|
#include "aggregation.hpp"
|
||||||
|
#include <map>
|
||||||
#include <algorithm>
|
#include <algorithm>
|
||||||
#include <limits>
|
#include <limits>
|
||||||
#include <cmath>
|
|
||||||
|
|
||||||
std::vector<DayStats> aggregate_days(const std::vector<Record>& records) {
|
std::vector<DayStats> aggregate_days(const std::vector<Record>& records) {
|
||||||
// Группируем записи по дням
|
// Накопители для каждого дня
|
||||||
std::map<DayIndex, std::vector<const Record*>> day_records;
|
struct DayAccumulator {
|
||||||
|
double avg_sum = 0.0;
|
||||||
|
double open_min = std::numeric_limits<double>::max();
|
||||||
|
double open_max = std::numeric_limits<double>::lowest();
|
||||||
|
double close_min = std::numeric_limits<double>::max();
|
||||||
|
double close_max = std::numeric_limits<double>::lowest();
|
||||||
|
int64_t count = 0;
|
||||||
|
};
|
||||||
|
|
||||||
|
std::map<DayIndex, DayAccumulator> days;
|
||||||
|
|
||||||
for (const auto& r : records) {
|
for (const auto& r : records) {
|
||||||
DayIndex day = static_cast<DayIndex>(r.timestamp) / 86400;
|
DayIndex day = static_cast<DayIndex>(r.timestamp) / 86400;
|
||||||
day_records[day].push_back(&r);
|
auto& acc = days[day];
|
||||||
|
|
||||||
|
double avg = (r.low + r.high) / 2.0;
|
||||||
|
acc.avg_sum += avg;
|
||||||
|
acc.open_min = std::min(acc.open_min, r.open);
|
||||||
|
acc.open_max = std::max(acc.open_max, r.open);
|
||||||
|
acc.close_min = std::min(acc.close_min, r.close);
|
||||||
|
acc.close_max = std::max(acc.close_max, r.close);
|
||||||
|
acc.count++;
|
||||||
}
|
}
|
||||||
|
|
||||||
std::vector<DayStats> result;
|
std::vector<DayStats> result;
|
||||||
result.reserve(day_records.size());
|
result.reserve(days.size());
|
||||||
|
|
||||||
for (auto& [day, recs] : day_records) {
|
for (const auto& [day, acc] : days) {
|
||||||
// Сортируем по timestamp для определения first/last
|
|
||||||
std::sort(recs.begin(), recs.end(),
|
|
||||||
[](const Record* a, const Record* b) {
|
|
||||||
return a->timestamp < b->timestamp;
|
|
||||||
});
|
|
||||||
|
|
||||||
DayStats stats;
|
DayStats stats;
|
||||||
stats.day = day;
|
stats.day = day;
|
||||||
stats.low = std::numeric_limits<double>::max();
|
stats.avg = acc.avg_sum / static_cast<double>(acc.count);
|
||||||
stats.high = std::numeric_limits<double>::lowest();
|
stats.open_min = acc.open_min;
|
||||||
stats.open = recs.front()->open;
|
stats.open_max = acc.open_max;
|
||||||
stats.close = recs.back()->close;
|
stats.close_min = acc.close_min;
|
||||||
stats.first_ts = recs.front()->timestamp;
|
stats.close_max = acc.close_max;
|
||||||
stats.last_ts = recs.back()->timestamp;
|
stats.count = acc.count;
|
||||||
|
|
||||||
for (const auto* r : recs) {
|
|
||||||
stats.low = std::min(stats.low, r->low);
|
|
||||||
stats.high = std::max(stats.high, r->high);
|
|
||||||
}
|
|
||||||
|
|
||||||
stats.avg = (stats.low + stats.high) / 2.0;
|
|
||||||
|
|
||||||
result.push_back(stats);
|
result.push_back(stats);
|
||||||
}
|
}
|
||||||
|
|
||||||
return result;
|
return result;
|
||||||
}
|
}
|
||||||
|
|
||||||
std::vector<DayStats> merge_day_stats(const std::vector<DayStats>& all_stats) {
|
|
||||||
// Объединяем статистику по одинаковым дням (если такие есть)
|
|
||||||
std::map<DayIndex, DayStats> merged;
|
|
||||||
|
|
||||||
for (const auto& s : all_stats) {
|
|
||||||
auto it = merged.find(s.day);
|
|
||||||
if (it == merged.end()) {
|
|
||||||
merged[s.day] = s;
|
|
||||||
} else {
|
|
||||||
// Объединяем данные за один день
|
|
||||||
auto& m = it->second;
|
|
||||||
m.low = std::min(m.low, s.low);
|
|
||||||
m.high = std::max(m.high, s.high);
|
|
||||||
|
|
||||||
// open берём от записи с меньшим timestamp
|
|
||||||
if (s.first_ts < m.first_ts) {
|
|
||||||
m.open = s.open;
|
|
||||||
m.first_ts = s.first_ts;
|
|
||||||
}
|
|
||||||
|
|
||||||
// close берём от записи с большим timestamp
|
|
||||||
if (s.last_ts > m.last_ts) {
|
|
||||||
m.close = s.close;
|
|
||||||
m.last_ts = s.last_ts;
|
|
||||||
}
|
|
||||||
|
|
||||||
m.avg = (m.low + m.high) / 2.0;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Преобразуем в отсортированный вектор
|
|
||||||
std::vector<DayStats> result;
|
|
||||||
result.reserve(merged.size());
|
|
||||||
|
|
||||||
for (auto& [day, stats] : merged) {
|
|
||||||
result.push_back(stats);
|
|
||||||
}
|
|
||||||
|
|
||||||
return result;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|||||||
@@ -3,12 +3,6 @@
|
|||||||
#include "record.hpp"
|
#include "record.hpp"
|
||||||
#include "day_stats.hpp"
|
#include "day_stats.hpp"
|
||||||
#include <vector>
|
#include <vector>
|
||||||
#include <map>
|
|
||||||
|
|
||||||
// Агрегация записей по дням на одном узле
|
// Агрегация записей по дням на одном узле
|
||||||
std::vector<DayStats> aggregate_days(const std::vector<Record>& records);
|
std::vector<DayStats> aggregate_days(const std::vector<Record>& records);
|
||||||
|
|
||||||
// Объединение агрегированных данных с разных узлов
|
|
||||||
// (на случай если один день попал на разные узлы - но в нашей схеме это не должно случиться)
|
|
||||||
std::vector<DayStats> merge_day_stats(const std::vector<DayStats>& all_stats);
|
|
||||||
|
|
||||||
|
|||||||
@@ -2,46 +2,134 @@
|
|||||||
#include <fstream>
|
#include <fstream>
|
||||||
#include <sstream>
|
#include <sstream>
|
||||||
#include <iostream>
|
#include <iostream>
|
||||||
|
#include <stdexcept>
|
||||||
|
|
||||||
std::vector<Record> load_csv(const std::string& filename) {
|
bool parse_csv_line(const std::string& line, Record& record) {
|
||||||
|
if (line.empty()) {
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::stringstream ss(line);
|
||||||
|
std::string item;
|
||||||
|
|
||||||
|
try {
|
||||||
|
// timestamp
|
||||||
|
if (!std::getline(ss, item, ',') || item.empty()) return false;
|
||||||
|
record.timestamp = std::stod(item);
|
||||||
|
|
||||||
|
// open
|
||||||
|
if (!std::getline(ss, item, ',') || item.empty()) return false;
|
||||||
|
record.open = std::stod(item);
|
||||||
|
|
||||||
|
// high
|
||||||
|
if (!std::getline(ss, item, ',') || item.empty()) return false;
|
||||||
|
record.high = std::stod(item);
|
||||||
|
|
||||||
|
// low
|
||||||
|
if (!std::getline(ss, item, ',') || item.empty()) return false;
|
||||||
|
record.low = std::stod(item);
|
||||||
|
|
||||||
|
// close
|
||||||
|
if (!std::getline(ss, item, ',') || item.empty()) return false;
|
||||||
|
record.close = std::stod(item);
|
||||||
|
|
||||||
|
// volume
|
||||||
|
if (!std::getline(ss, item, ',')) return false;
|
||||||
|
// Volume может быть пустым или содержать данные
|
||||||
|
if (item.empty()) {
|
||||||
|
record.volume = 0.0;
|
||||||
|
} else {
|
||||||
|
record.volume = std::stod(item);
|
||||||
|
}
|
||||||
|
|
||||||
|
return true;
|
||||||
|
} catch (const std::exception&) {
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<Record> load_csv_parallel(int rank, int size) {
|
||||||
std::vector<Record> data;
|
std::vector<Record> data;
|
||||||
std::ifstream file(filename);
|
|
||||||
|
// Читаем настройки из переменных окружения
|
||||||
|
std::string data_path = get_data_path();
|
||||||
|
std::vector<int> shares = get_data_read_shares();
|
||||||
|
int64_t overlap_bytes = get_read_overlap_bytes();
|
||||||
|
|
||||||
|
// Получаем размер файла
|
||||||
|
int64_t file_size = get_file_size(data_path);
|
||||||
|
|
||||||
|
// Вычисляем диапазон байт для этого ранка
|
||||||
|
ByteRange range = calculate_byte_range(rank, size, file_size, shares, overlap_bytes);
|
||||||
|
|
||||||
|
// Открываем файл и читаем нужный диапазон
|
||||||
|
std::ifstream file(data_path, std::ios::binary);
|
||||||
if (!file.is_open()) {
|
if (!file.is_open()) {
|
||||||
throw std::runtime_error("Cannot open file: " + filename);
|
throw std::runtime_error("Cannot open file: " + data_path);
|
||||||
}
|
}
|
||||||
|
|
||||||
std::string line;
|
// Переходим к началу диапазона
|
||||||
|
file.seekg(range.start);
|
||||||
// читаем первую строку (заголовок)
|
|
||||||
std::getline(file, line);
|
// Читаем данные в буфер
|
||||||
|
int64_t bytes_to_read = range.end - range.start;
|
||||||
while (std::getline(file, line)) {
|
std::vector<char> buffer(bytes_to_read);
|
||||||
std::stringstream ss(line);
|
file.read(buffer.data(), bytes_to_read);
|
||||||
std::string item;
|
int64_t bytes_read = file.gcount();
|
||||||
|
|
||||||
Record row;
|
file.close();
|
||||||
|
|
||||||
std::getline(ss, item, ',');
|
// Преобразуем в строку для удобства парсинга
|
||||||
row.timestamp = std::stod(item);
|
std::string content(buffer.data(), bytes_read);
|
||||||
|
|
||||||
std::getline(ss, item, ',');
|
// Находим позицию начала первой полной строки
|
||||||
row.open = std::stod(item);
|
size_t parse_start = 0;
|
||||||
|
if (rank == 0) {
|
||||||
std::getline(ss, item, ',');
|
// Первый ранк: пропускаем заголовок (первую строку)
|
||||||
row.high = std::stod(item);
|
size_t header_end = content.find('\n');
|
||||||
|
if (header_end != std::string::npos) {
|
||||||
std::getline(ss, item, ',');
|
parse_start = header_end + 1;
|
||||||
row.low = std::stod(item);
|
}
|
||||||
|
} else {
|
||||||
std::getline(ss, item, ',');
|
// Остальные ранки: начинаем с первого \n (пропускаем неполную строку)
|
||||||
row.close = std::stod(item);
|
size_t first_newline = content.find('\n');
|
||||||
|
if (first_newline != std::string::npos) {
|
||||||
std::getline(ss, item, ',');
|
parse_start = first_newline + 1;
|
||||||
row.volume = std::stod(item);
|
}
|
||||||
|
|
||||||
data.push_back(row);
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Находим позицию конца последней полной строки
|
||||||
|
size_t parse_end = content.size();
|
||||||
|
if (rank != size - 1) {
|
||||||
|
// Не последний ранк: ищем последний \n
|
||||||
|
size_t last_newline = content.rfind('\n');
|
||||||
|
if (last_newline != std::string::npos && last_newline > parse_start) {
|
||||||
|
parse_end = last_newline;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Парсим строки
|
||||||
|
size_t pos = parse_start;
|
||||||
|
while (pos < parse_end) {
|
||||||
|
size_t line_end = content.find('\n', pos);
|
||||||
|
if (line_end == std::string::npos || line_end > parse_end) {
|
||||||
|
line_end = parse_end;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::string line = content.substr(pos, line_end - pos);
|
||||||
|
|
||||||
|
// Убираем \r если есть (Windows line endings)
|
||||||
|
if (!line.empty() && line.back() == '\r') {
|
||||||
|
line.pop_back();
|
||||||
|
}
|
||||||
|
|
||||||
|
Record record;
|
||||||
|
if (parse_csv_line(line, record)) {
|
||||||
|
data.push_back(record);
|
||||||
|
}
|
||||||
|
|
||||||
|
pos = line_end + 1;
|
||||||
|
}
|
||||||
|
|
||||||
return data;
|
return data;
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -2,5 +2,14 @@
|
|||||||
#include <string>
|
#include <string>
|
||||||
#include <vector>
|
#include <vector>
|
||||||
#include "record.hpp"
|
#include "record.hpp"
|
||||||
|
#include "utils.hpp"
|
||||||
|
|
||||||
std::vector<Record> load_csv(const std::string& filename);
|
// Параллельное чтение CSV файла для MPI
|
||||||
|
// rank - номер текущего ранка
|
||||||
|
// size - общее количество ранков
|
||||||
|
// Возвращает вектор записей, прочитанных этим ранком
|
||||||
|
std::vector<Record> load_csv_parallel(int rank, int size);
|
||||||
|
|
||||||
|
// Парсинг одной строки CSV в Record
|
||||||
|
// Возвращает true если парсинг успешен
|
||||||
|
bool parse_csv_line(const std::string& line, Record& record);
|
||||||
|
|||||||
@@ -1,28 +1,15 @@
|
|||||||
#pragma once
|
#pragma once
|
||||||
#include <cstdint>
|
#include <cstdint>
|
||||||
|
|
||||||
using DayIndex = long long;
|
using DayIndex = int64_t;
|
||||||
|
|
||||||
// Агрегированные данные за один день
|
// Агрегированные данные за один день
|
||||||
struct DayStats {
|
struct DayStats {
|
||||||
DayIndex day; // индекс дня (timestamp / 86400)
|
DayIndex day; // индекс дня (timestamp / 86400)
|
||||||
double low; // минимальный Low за день
|
double avg; // среднее значение (Low + High) / 2 по всем записям
|
||||||
double high; // максимальный High за день
|
double open_min; // минимальный Open за день
|
||||||
double open; // первый Open за день
|
double open_max; // максимальный Open за день
|
||||||
double close; // последний Close за день
|
double close_min; // минимальный Close за день
|
||||||
double avg; // среднее = (low + high) / 2
|
double close_max; // максимальный Close за день
|
||||||
double first_ts; // timestamp первой записи (для определения порядка open)
|
int64_t count; // количество записей, по которым агрегировали
|
||||||
double last_ts; // timestamp последней записи (для определения close)
|
|
||||||
};
|
};
|
||||||
|
|
||||||
// Интервал с изменением >= 10%
|
|
||||||
struct Interval {
|
|
||||||
DayIndex start_day;
|
|
||||||
DayIndex end_day;
|
|
||||||
double min_open;
|
|
||||||
double max_close;
|
|
||||||
double start_avg;
|
|
||||||
double end_avg;
|
|
||||||
double change;
|
|
||||||
};
|
|
||||||
|
|
||||||
|
|||||||
@@ -2,6 +2,9 @@
|
|||||||
#include <dlfcn.h>
|
#include <dlfcn.h>
|
||||||
#include <map>
|
#include <map>
|
||||||
#include <algorithm>
|
#include <algorithm>
|
||||||
|
#include <iostream>
|
||||||
|
#include <iomanip>
|
||||||
|
#include <omp.h>
|
||||||
|
|
||||||
static void* get_gpu_lib_handle() {
|
static void* get_gpu_lib_handle() {
|
||||||
static void* h = dlopen("./libgpu_compute.so", RTLD_NOW | RTLD_LOCAL);
|
static void* h = dlopen("./libgpu_compute.so", RTLD_NOW | RTLD_LOCAL);
|
||||||
@@ -16,6 +19,16 @@ gpu_is_available_fn load_gpu_is_available() {
|
|||||||
return fn;
|
return fn;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
bool gpu_is_available() {
|
||||||
|
auto gpu_is_available_fn = load_gpu_is_available();
|
||||||
|
|
||||||
|
if (gpu_is_available_fn && gpu_is_available_fn()) {
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
gpu_aggregate_days_fn load_gpu_aggregate_days() {
|
gpu_aggregate_days_fn load_gpu_aggregate_days() {
|
||||||
void* h = get_gpu_lib_handle();
|
void* h = get_gpu_lib_handle();
|
||||||
if (!h) return nullptr;
|
if (!h) return nullptr;
|
||||||
@@ -33,6 +46,12 @@ bool aggregate_days_gpu(
|
|||||||
return false;
|
return false;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Общий таймер всей функции
|
||||||
|
double t_total_start = omp_get_wtime();
|
||||||
|
|
||||||
|
// Таймер CPU preprocessing
|
||||||
|
double t_preprocess_start = omp_get_wtime();
|
||||||
|
|
||||||
// Группируем записи по дням и подготавливаем данные для GPU
|
// Группируем записи по дням и подготавливаем данные для GPU
|
||||||
std::map<DayIndex, std::vector<size_t>> day_record_indices;
|
std::map<DayIndex, std::vector<size_t>> day_record_indices;
|
||||||
|
|
||||||
@@ -80,7 +99,12 @@ bool aggregate_days_gpu(
|
|||||||
// Выделяем память для результата
|
// Выделяем память для результата
|
||||||
std::vector<GpuDayStats> gpu_stats(num_days);
|
std::vector<GpuDayStats> gpu_stats(num_days);
|
||||||
|
|
||||||
// Вызываем GPU функцию
|
double t_preprocess_ms = (omp_get_wtime() - t_preprocess_start) * 1000.0;
|
||||||
|
std::cout << " GPU CPU preprocessing: " << std::fixed << std::setprecision(3)
|
||||||
|
<< std::setw(7) << t_preprocess_ms << " ms" << std::endl << std::flush;
|
||||||
|
|
||||||
|
// Вызываем GPU функцию (включает: malloc, memcpy H->D, kernel, memcpy D->H, free)
|
||||||
|
// Детальные тайминги выводятся внутри GPU функции
|
||||||
int result = gpu_fn(
|
int result = gpu_fn(
|
||||||
gpu_records.data(),
|
gpu_records.data(),
|
||||||
static_cast<int>(gpu_records.size()),
|
static_cast<int>(gpu_records.size()),
|
||||||
@@ -92,6 +116,7 @@ bool aggregate_days_gpu(
|
|||||||
);
|
);
|
||||||
|
|
||||||
if (result != 0) {
|
if (result != 0) {
|
||||||
|
std::cout << " GPU: Function returned error code " << result << std::endl;
|
||||||
return false;
|
return false;
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -102,15 +127,19 @@ bool aggregate_days_gpu(
|
|||||||
for (const auto& gs : gpu_stats) {
|
for (const auto& gs : gpu_stats) {
|
||||||
DayStats ds;
|
DayStats ds;
|
||||||
ds.day = gs.day;
|
ds.day = gs.day;
|
||||||
ds.low = gs.low;
|
|
||||||
ds.high = gs.high;
|
|
||||||
ds.open = gs.open;
|
|
||||||
ds.close = gs.close;
|
|
||||||
ds.avg = gs.avg;
|
ds.avg = gs.avg;
|
||||||
ds.first_ts = gs.first_ts;
|
ds.open_min = gs.open_min;
|
||||||
ds.last_ts = gs.last_ts;
|
ds.open_max = gs.open_max;
|
||||||
|
ds.close_min = gs.close_min;
|
||||||
|
ds.close_max = gs.close_max;
|
||||||
|
ds.count = gs.count;
|
||||||
out_stats.push_back(ds);
|
out_stats.push_back(ds);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Общее время всей GPU функции (включая preprocessing)
|
||||||
|
double t_total_ms = (omp_get_wtime() - t_total_start) * 1000.0;
|
||||||
|
std::cout << " GPU TOTAL (with prep): " << std::fixed << std::setprecision(3)
|
||||||
|
<< std::setw(7) << t_total_ms << " ms" << std::endl << std::flush;
|
||||||
|
|
||||||
return true;
|
return true;
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -3,6 +3,8 @@
|
|||||||
#include "record.hpp"
|
#include "record.hpp"
|
||||||
#include <vector>
|
#include <vector>
|
||||||
|
|
||||||
|
bool gpu_is_available();
|
||||||
|
|
||||||
// Типы функций из GPU плагина
|
// Типы функций из GPU плагина
|
||||||
using gpu_is_available_fn = int (*)();
|
using gpu_is_available_fn = int (*)();
|
||||||
|
|
||||||
@@ -18,13 +20,12 @@ struct GpuRecord {
|
|||||||
|
|
||||||
struct GpuDayStats {
|
struct GpuDayStats {
|
||||||
long long day;
|
long long day;
|
||||||
double low;
|
|
||||||
double high;
|
|
||||||
double open;
|
|
||||||
double close;
|
|
||||||
double avg;
|
double avg;
|
||||||
double first_ts;
|
double open_min;
|
||||||
double last_ts;
|
double open_max;
|
||||||
|
double close_min;
|
||||||
|
double close_max;
|
||||||
|
long long count;
|
||||||
};
|
};
|
||||||
|
|
||||||
using gpu_aggregate_days_fn = int (*)(
|
using gpu_aggregate_days_fn = int (*)(
|
||||||
|
|||||||
@@ -1,6 +1,15 @@
|
|||||||
#include <cuda_runtime.h>
|
#include <cuda_runtime.h>
|
||||||
#include <cstdint>
|
#include <cstdint>
|
||||||
#include <cfloat>
|
#include <cfloat>
|
||||||
|
#include <cstdio>
|
||||||
|
#include <ctime>
|
||||||
|
|
||||||
|
// CPU таймер в миллисекундах
|
||||||
|
static double get_time_ms() {
|
||||||
|
struct timespec ts;
|
||||||
|
clock_gettime(CLOCK_MONOTONIC, &ts);
|
||||||
|
return ts.tv_sec * 1000.0 + ts.tv_nsec / 1000000.0;
|
||||||
|
}
|
||||||
|
|
||||||
// Структуры данных (должны совпадать с C++ кодом)
|
// Структуры данных (должны совпадать с C++ кодом)
|
||||||
struct GpuRecord {
|
struct GpuRecord {
|
||||||
@@ -14,19 +23,22 @@ struct GpuRecord {
|
|||||||
|
|
||||||
struct GpuDayStats {
|
struct GpuDayStats {
|
||||||
long long day;
|
long long day;
|
||||||
double low;
|
|
||||||
double high;
|
|
||||||
double open;
|
|
||||||
double close;
|
|
||||||
double avg;
|
double avg;
|
||||||
double first_ts;
|
double open_min;
|
||||||
double last_ts;
|
double open_max;
|
||||||
|
double close_min;
|
||||||
|
double close_max;
|
||||||
|
long long count;
|
||||||
};
|
};
|
||||||
|
|
||||||
extern "C" int gpu_is_available() {
|
extern "C" int gpu_is_available() {
|
||||||
int n = 0;
|
int n = 0;
|
||||||
cudaError_t err = cudaGetDeviceCount(&n);
|
cudaError_t err = cudaGetDeviceCount(&n);
|
||||||
if (err != cudaSuccess) return 0;
|
if (err != cudaSuccess) return 0;
|
||||||
|
if (n > 0) {
|
||||||
|
// Инициализируем CUDA контекст заранее (cudaFree(0) форсирует инициализацию)
|
||||||
|
cudaFree(0);
|
||||||
|
}
|
||||||
return (n > 0) ? 1 : 0;
|
return (n > 0) ? 1 : 0;
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -50,32 +62,30 @@ __global__ void aggregate_kernel(
|
|||||||
|
|
||||||
GpuDayStats stats;
|
GpuDayStats stats;
|
||||||
stats.day = day_indices[d];
|
stats.day = day_indices[d];
|
||||||
stats.low = DBL_MAX;
|
stats.open_min = DBL_MAX;
|
||||||
stats.high = -DBL_MAX;
|
stats.open_max = -DBL_MAX;
|
||||||
stats.first_ts = DBL_MAX;
|
stats.close_min = DBL_MAX;
|
||||||
stats.last_ts = -DBL_MAX;
|
stats.close_max = -DBL_MAX;
|
||||||
stats.open = 0;
|
stats.count = count;
|
||||||
stats.close = 0;
|
|
||||||
|
double avg_sum = 0.0;
|
||||||
|
|
||||||
for (int i = 0; i < count; i++) {
|
for (int i = 0; i < count; i++) {
|
||||||
const GpuRecord& r = records[offset + i];
|
const GpuRecord& r = records[offset + i];
|
||||||
|
|
||||||
// min/max
|
// Accumulate avg = (low + high) / 2
|
||||||
if (r.low < stats.low) stats.low = r.low;
|
avg_sum += (r.low + r.high) / 2.0;
|
||||||
if (r.high > stats.high) stats.high = r.high;
|
|
||||||
|
|
||||||
// first/last по timestamp
|
// min/max Open
|
||||||
if (r.timestamp < stats.first_ts) {
|
if (r.open < stats.open_min) stats.open_min = r.open;
|
||||||
stats.first_ts = r.timestamp;
|
if (r.open > stats.open_max) stats.open_max = r.open;
|
||||||
stats.open = r.open;
|
|
||||||
}
|
// min/max Close
|
||||||
if (r.timestamp > stats.last_ts) {
|
if (r.close < stats.close_min) stats.close_min = r.close;
|
||||||
stats.last_ts = r.timestamp;
|
if (r.close > stats.close_max) stats.close_max = r.close;
|
||||||
stats.close = r.close;
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
|
||||||
stats.avg = (stats.low + stats.high) / 2.0;
|
stats.avg = avg_sum / static_cast<double>(count);
|
||||||
out_stats[d] = stats;
|
out_stats[d] = stats;
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -89,7 +99,33 @@ extern "C" int gpu_aggregate_days(
|
|||||||
int num_days,
|
int num_days,
|
||||||
GpuDayStats* h_out_stats)
|
GpuDayStats* h_out_stats)
|
||||||
{
|
{
|
||||||
// Выделяем память на GPU
|
double cpu_total_start = get_time_ms();
|
||||||
|
|
||||||
|
// === Создаём CUDA события для измерения времени ===
|
||||||
|
double cpu_event_create_start = get_time_ms();
|
||||||
|
|
||||||
|
cudaEvent_t start_malloc, stop_malloc;
|
||||||
|
cudaEvent_t start_transfer, stop_transfer;
|
||||||
|
cudaEvent_t start_kernel, stop_kernel;
|
||||||
|
cudaEvent_t start_copy_back, stop_copy_back;
|
||||||
|
cudaEvent_t start_free, stop_free;
|
||||||
|
|
||||||
|
cudaEventCreate(&start_malloc);
|
||||||
|
cudaEventCreate(&stop_malloc);
|
||||||
|
cudaEventCreate(&start_transfer);
|
||||||
|
cudaEventCreate(&stop_transfer);
|
||||||
|
cudaEventCreate(&start_kernel);
|
||||||
|
cudaEventCreate(&stop_kernel);
|
||||||
|
cudaEventCreate(&start_copy_back);
|
||||||
|
cudaEventCreate(&stop_copy_back);
|
||||||
|
cudaEventCreate(&start_free);
|
||||||
|
cudaEventCreate(&stop_free);
|
||||||
|
|
||||||
|
double cpu_event_create_ms = get_time_ms() - cpu_event_create_start;
|
||||||
|
|
||||||
|
// === ИЗМЕРЕНИЕ cudaMalloc ===
|
||||||
|
cudaEventRecord(start_malloc);
|
||||||
|
|
||||||
GpuRecord* d_records = nullptr;
|
GpuRecord* d_records = nullptr;
|
||||||
int* d_day_offsets = nullptr;
|
int* d_day_offsets = nullptr;
|
||||||
int* d_day_counts = nullptr;
|
int* d_day_counts = nullptr;
|
||||||
@@ -113,7 +149,15 @@ extern "C" int gpu_aggregate_days(
|
|||||||
err = cudaMalloc(&d_out_stats, num_days * sizeof(GpuDayStats));
|
err = cudaMalloc(&d_out_stats, num_days * sizeof(GpuDayStats));
|
||||||
if (err != cudaSuccess) { cudaFree(d_records); cudaFree(d_day_offsets); cudaFree(d_day_counts); cudaFree(d_day_indices); return -5; }
|
if (err != cudaSuccess) { cudaFree(d_records); cudaFree(d_day_offsets); cudaFree(d_day_counts); cudaFree(d_day_indices); return -5; }
|
||||||
|
|
||||||
// Копируем данные на GPU
|
cudaEventRecord(stop_malloc);
|
||||||
|
cudaEventSynchronize(stop_malloc);
|
||||||
|
|
||||||
|
float time_malloc_ms = 0;
|
||||||
|
cudaEventElapsedTime(&time_malloc_ms, start_malloc, stop_malloc);
|
||||||
|
|
||||||
|
// === ИЗМЕРЕНИЕ memcpy H->D ===
|
||||||
|
cudaEventRecord(start_transfer);
|
||||||
|
|
||||||
err = cudaMemcpy(d_records, h_records, num_records * sizeof(GpuRecord), cudaMemcpyHostToDevice);
|
err = cudaMemcpy(d_records, h_records, num_records * sizeof(GpuRecord), cudaMemcpyHostToDevice);
|
||||||
if (err != cudaSuccess) return -10;
|
if (err != cudaSuccess) return -10;
|
||||||
|
|
||||||
@@ -126,17 +170,24 @@ extern "C" int gpu_aggregate_days(
|
|||||||
err = cudaMemcpy(d_day_indices, h_day_indices, num_days * sizeof(long long), cudaMemcpyHostToDevice);
|
err = cudaMemcpy(d_day_indices, h_day_indices, num_days * sizeof(long long), cudaMemcpyHostToDevice);
|
||||||
if (err != cudaSuccess) return -13;
|
if (err != cudaSuccess) return -13;
|
||||||
|
|
||||||
// Запускаем kernel: каждый поток обрабатывает один день
|
cudaEventRecord(stop_transfer);
|
||||||
|
cudaEventSynchronize(stop_transfer);
|
||||||
|
|
||||||
|
float time_transfer_ms = 0;
|
||||||
|
cudaEventElapsedTime(&time_transfer_ms, start_transfer, stop_transfer);
|
||||||
|
|
||||||
|
// === ИЗМЕРЕНИЕ kernel ===
|
||||||
const int THREADS_PER_BLOCK = 256;
|
const int THREADS_PER_BLOCK = 256;
|
||||||
int num_blocks = (num_days + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;
|
int num_blocks = (num_days + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;
|
||||||
|
|
||||||
|
cudaEventRecord(start_kernel);
|
||||||
|
|
||||||
aggregate_kernel<<<num_blocks, THREADS_PER_BLOCK>>>(
|
aggregate_kernel<<<num_blocks, THREADS_PER_BLOCK>>>(
|
||||||
d_records, num_records,
|
d_records, num_records,
|
||||||
d_day_offsets, d_day_counts, d_day_indices,
|
d_day_offsets, d_day_counts, d_day_indices,
|
||||||
num_days, d_out_stats
|
num_days, d_out_stats
|
||||||
);
|
);
|
||||||
|
|
||||||
// Проверяем ошибку запуска kernel
|
|
||||||
err = cudaGetLastError();
|
err = cudaGetLastError();
|
||||||
if (err != cudaSuccess) {
|
if (err != cudaSuccess) {
|
||||||
cudaFree(d_records);
|
cudaFree(d_records);
|
||||||
@@ -147,26 +198,65 @@ extern "C" int gpu_aggregate_days(
|
|||||||
return -7;
|
return -7;
|
||||||
}
|
}
|
||||||
|
|
||||||
// Ждём завершения
|
cudaEventRecord(stop_kernel);
|
||||||
err = cudaDeviceSynchronize();
|
cudaEventSynchronize(stop_kernel);
|
||||||
if (err != cudaSuccess) {
|
|
||||||
cudaFree(d_records);
|
|
||||||
cudaFree(d_day_offsets);
|
|
||||||
cudaFree(d_day_counts);
|
|
||||||
cudaFree(d_day_indices);
|
|
||||||
cudaFree(d_out_stats);
|
|
||||||
return -6;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Копируем результат обратно
|
float time_kernel_ms = 0;
|
||||||
|
cudaEventElapsedTime(&time_kernel_ms, start_kernel, stop_kernel);
|
||||||
|
|
||||||
|
// === ИЗМЕРЕНИЕ memcpy D->H ===
|
||||||
|
cudaEventRecord(start_copy_back);
|
||||||
cudaMemcpy(h_out_stats, d_out_stats, num_days * sizeof(GpuDayStats), cudaMemcpyDeviceToHost);
|
cudaMemcpy(h_out_stats, d_out_stats, num_days * sizeof(GpuDayStats), cudaMemcpyDeviceToHost);
|
||||||
|
cudaEventRecord(stop_copy_back);
|
||||||
|
cudaEventSynchronize(stop_copy_back);
|
||||||
|
|
||||||
|
float time_copy_back_ms = 0;
|
||||||
|
cudaEventElapsedTime(&time_copy_back_ms, start_copy_back, stop_copy_back);
|
||||||
|
|
||||||
|
// === ИЗМЕРЕНИЕ cudaFree ===
|
||||||
|
cudaEventRecord(start_free);
|
||||||
|
|
||||||
// Освобождаем память
|
|
||||||
cudaFree(d_records);
|
cudaFree(d_records);
|
||||||
cudaFree(d_day_offsets);
|
cudaFree(d_day_offsets);
|
||||||
cudaFree(d_day_counts);
|
cudaFree(d_day_counts);
|
||||||
cudaFree(d_day_indices);
|
cudaFree(d_day_indices);
|
||||||
cudaFree(d_out_stats);
|
cudaFree(d_out_stats);
|
||||||
|
|
||||||
|
cudaEventRecord(stop_free);
|
||||||
|
cudaEventSynchronize(stop_free);
|
||||||
|
|
||||||
|
float time_free_ms = 0;
|
||||||
|
cudaEventElapsedTime(&time_free_ms, start_free, stop_free);
|
||||||
|
|
||||||
|
// Общее время GPU
|
||||||
|
float time_total_ms = time_malloc_ms + time_transfer_ms + time_kernel_ms + time_copy_back_ms + time_free_ms;
|
||||||
|
|
||||||
|
// === Освобождаем события ===
|
||||||
|
double cpu_event_destroy_start = get_time_ms();
|
||||||
|
|
||||||
|
cudaEventDestroy(start_malloc);
|
||||||
|
cudaEventDestroy(stop_malloc);
|
||||||
|
cudaEventDestroy(start_transfer);
|
||||||
|
cudaEventDestroy(stop_transfer);
|
||||||
|
cudaEventDestroy(start_kernel);
|
||||||
|
cudaEventDestroy(stop_kernel);
|
||||||
|
cudaEventDestroy(start_copy_back);
|
||||||
|
cudaEventDestroy(stop_copy_back);
|
||||||
|
cudaEventDestroy(start_free);
|
||||||
|
cudaEventDestroy(stop_free);
|
||||||
|
|
||||||
|
double cpu_event_destroy_ms = get_time_ms() - cpu_event_destroy_start;
|
||||||
|
double cpu_total_ms = get_time_ms() - cpu_total_start;
|
||||||
|
|
||||||
|
// Выводим детальную статистику
|
||||||
|
printf(" GPU Timings (%d records, %d days):\n", num_records, num_days);
|
||||||
|
printf(" cudaMalloc: %7.3f ms\n", time_malloc_ms);
|
||||||
|
printf(" memcpy H->D: %7.3f ms\n", time_transfer_ms);
|
||||||
|
printf(" kernel execution: %7.3f ms\n", time_kernel_ms);
|
||||||
|
printf(" memcpy D->H: %7.3f ms\n", time_copy_back_ms);
|
||||||
|
printf(" cudaFree: %7.3f ms\n", time_free_ms);
|
||||||
|
printf(" GPU TOTAL: %7.3f ms\n", cpu_total_ms);
|
||||||
|
fflush(stdout);
|
||||||
|
|
||||||
return 0;
|
return 0;
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -28,13 +28,17 @@ std::vector<Interval> find_intervals(const std::vector<DayStats>& days, double t
|
|||||||
interval.end_avg = price_now;
|
interval.end_avg = price_now;
|
||||||
interval.change = change;
|
interval.change = change;
|
||||||
|
|
||||||
// Находим min(Open) и max(Close) в интервале
|
// Находим min/max Open и Close в интервале
|
||||||
interval.min_open = days[start_idx].open;
|
interval.open_min = days[start_idx].open_min;
|
||||||
interval.max_close = days[start_idx].close;
|
interval.open_max = days[start_idx].open_max;
|
||||||
|
interval.close_min = days[start_idx].close_min;
|
||||||
|
interval.close_max = days[start_idx].close_max;
|
||||||
|
|
||||||
for (size_t j = start_idx; j <= i; j++) {
|
for (size_t j = start_idx + 1; j <= i; j++) {
|
||||||
interval.min_open = std::min(interval.min_open, days[j].open);
|
interval.open_min = std::min(interval.open_min, days[j].open_min);
|
||||||
interval.max_close = std::max(interval.max_close, days[j].close);
|
interval.open_max = std::max(interval.open_max, days[j].open_max);
|
||||||
|
interval.close_min = std::min(interval.close_min, days[j].close_min);
|
||||||
|
interval.close_max = std::max(interval.close_max, days[j].close_max);
|
||||||
}
|
}
|
||||||
|
|
||||||
intervals.push_back(interval);
|
intervals.push_back(interval);
|
||||||
@@ -68,16 +72,17 @@ void write_intervals(const std::string& filename, const std::vector<Interval>& i
|
|||||||
std::ofstream out(filename);
|
std::ofstream out(filename);
|
||||||
|
|
||||||
out << std::fixed << std::setprecision(2);
|
out << std::fixed << std::setprecision(2);
|
||||||
out << "start_date,end_date,min_open,max_close,start_avg,end_avg,change\n";
|
out << "start_date,end_date,open_min,open_max,close_min,close_max,start_avg,end_avg,change\n";
|
||||||
|
|
||||||
for (const auto& iv : intervals) {
|
for (const auto& iv : intervals) {
|
||||||
out << day_index_to_date(iv.start_day) << ","
|
out << day_index_to_date(iv.start_day) << ","
|
||||||
<< day_index_to_date(iv.end_day) << ","
|
<< day_index_to_date(iv.end_day) << ","
|
||||||
<< iv.min_open << ","
|
<< iv.open_min << ","
|
||||||
<< iv.max_close << ","
|
<< iv.open_max << ","
|
||||||
|
<< iv.close_min << ","
|
||||||
|
<< iv.close_max << ","
|
||||||
<< iv.start_avg << ","
|
<< iv.start_avg << ","
|
||||||
<< iv.end_avg << ","
|
<< iv.end_avg << ","
|
||||||
<< std::setprecision(6) << iv.change << "\n";
|
<< std::setprecision(6) << iv.change << "\n";
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -4,6 +4,19 @@
|
|||||||
#include <vector>
|
#include <vector>
|
||||||
#include <string>
|
#include <string>
|
||||||
|
|
||||||
|
// Интервал с изменением >= threshold
|
||||||
|
struct Interval {
|
||||||
|
DayIndex start_day;
|
||||||
|
DayIndex end_day;
|
||||||
|
double open_min; // минимальный Open в интервале
|
||||||
|
double open_max; // максимальный Open в интервале
|
||||||
|
double close_min; // минимальный Close в интервале
|
||||||
|
double close_max; // максимальный Close в интервале
|
||||||
|
double start_avg;
|
||||||
|
double end_avg;
|
||||||
|
double change;
|
||||||
|
};
|
||||||
|
|
||||||
// Вычисление интервалов с изменением >= threshold (по умолчанию 10%)
|
// Вычисление интервалов с изменением >= threshold (по умолчанию 10%)
|
||||||
std::vector<Interval> find_intervals(const std::vector<DayStats>& days, double threshold = 0.10);
|
std::vector<Interval> find_intervals(const std::vector<DayStats>& days, double threshold = 0.10);
|
||||||
|
|
||||||
@@ -12,4 +25,3 @@ void write_intervals(const std::string& filename, const std::vector<Interval>& i
|
|||||||
|
|
||||||
// Преобразование DayIndex в строку даты (YYYY-MM-DD)
|
// Преобразование DayIndex в строку даты (YYYY-MM-DD)
|
||||||
std::string day_index_to_date(DayIndex day);
|
std::string day_index_to_date(DayIndex day);
|
||||||
|
|
||||||
|
|||||||
251
src/main.cpp
251
src/main.cpp
@@ -1,54 +1,12 @@
|
|||||||
#include <mpi.h>
|
#include <mpi.h>
|
||||||
#include <omp.h>
|
|
||||||
#include <iostream>
|
#include <iostream>
|
||||||
#include <vector>
|
#include <vector>
|
||||||
#include <map>
|
|
||||||
#include <iomanip>
|
#include <iomanip>
|
||||||
#include <cstdlib>
|
|
||||||
|
|
||||||
#include "csv_loader.hpp"
|
#include "csv_loader.hpp"
|
||||||
#include "utils.hpp"
|
|
||||||
#include "record.hpp"
|
#include "record.hpp"
|
||||||
#include "day_stats.hpp"
|
#include "day_stats.hpp"
|
||||||
#include "aggregation.hpp"
|
#include "aggregation.hpp"
|
||||||
#include "intervals.hpp"
|
|
||||||
#include "gpu_loader.hpp"
|
|
||||||
|
|
||||||
// Функция: отобрать записи для конкретного ранга
|
|
||||||
std::vector<Record> select_records_for_rank(
|
|
||||||
const std::map<DayIndex, std::vector<Record>>& days,
|
|
||||||
const std::vector<DayIndex>& day_list)
|
|
||||||
{
|
|
||||||
std::vector<Record> out;
|
|
||||||
for (auto d : day_list) {
|
|
||||||
auto it = days.find(d);
|
|
||||||
if (it != days.end()) {
|
|
||||||
const auto& vec = it->second;
|
|
||||||
out.insert(out.end(), vec.begin(), vec.end());
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return out;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Разделить записи на N частей (по дням)
|
|
||||||
std::vector<std::vector<Record>> split_records(const std::vector<Record>& records, int n_parts) {
|
|
||||||
// Группируем по дням
|
|
||||||
std::map<DayIndex, std::vector<Record>> by_day;
|
|
||||||
for (const auto& r : records) {
|
|
||||||
DayIndex day = static_cast<DayIndex>(r.timestamp) / 86400;
|
|
||||||
by_day[day].push_back(r);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Распределяем дни по частям
|
|
||||||
std::vector<std::vector<Record>> parts(n_parts);
|
|
||||||
int i = 0;
|
|
||||||
for (auto& [day, recs] : by_day) {
|
|
||||||
parts[i % n_parts].insert(parts[i % n_parts].end(), recs.begin(), recs.end());
|
|
||||||
i++;
|
|
||||||
}
|
|
||||||
|
|
||||||
return parts;
|
|
||||||
}
|
|
||||||
|
|
||||||
int main(int argc, char** argv) {
|
int main(int argc, char** argv) {
|
||||||
MPI_Init(&argc, &argv);
|
MPI_Init(&argc, &argv);
|
||||||
@@ -57,200 +15,27 @@ int main(int argc, char** argv) {
|
|||||||
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
|
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
|
||||||
MPI_Comm_size(MPI_COMM_WORLD, &size);
|
MPI_Comm_size(MPI_COMM_WORLD, &size);
|
||||||
|
|
||||||
// Читаем количество CPU потоков из переменной окружения (по умолчанию 2)
|
// Параллельное чтение данных
|
||||||
int num_cpu_threads = 2;
|
double read_start = MPI_Wtime();
|
||||||
const char* env_threads = std::getenv("NUM_CPU_THREADS");
|
std::vector<Record> records = load_csv_parallel(rank, size);
|
||||||
if (env_threads) {
|
double read_time = MPI_Wtime() - read_start;
|
||||||
num_cpu_threads = std::atoi(env_threads);
|
|
||||||
if (num_cpu_threads < 1) num_cpu_threads = 1;
|
|
||||||
}
|
|
||||||
omp_set_num_threads(num_cpu_threads + 1); // +1 для GPU потока если есть
|
|
||||||
|
|
||||||
// ====== ЗАГРУЗКА GPU ФУНКЦИЙ ======
|
std::cout << "Rank " << rank
|
||||||
auto gpu_is_available = load_gpu_is_available();
|
<< ": read " << records.size() << " records"
|
||||||
auto gpu_aggregate = load_gpu_aggregate_days();
|
<< " in " << std::fixed << std::setprecision(3) << read_time << " sec"
|
||||||
|
<< std::endl;
|
||||||
bool have_gpu = false;
|
|
||||||
if (gpu_is_available && gpu_is_available()) {
|
|
||||||
have_gpu = true;
|
|
||||||
std::cout << "Rank " << rank << ": GPU available + " << num_cpu_threads << " CPU threads" << std::endl;
|
|
||||||
} else {
|
|
||||||
std::cout << "Rank " << rank << ": " << num_cpu_threads << " CPU threads only" << std::endl;
|
|
||||||
}
|
|
||||||
|
|
||||||
std::vector<Record> local_records;
|
// Агрегация по дням
|
||||||
|
double agg_start = MPI_Wtime();
|
||||||
|
std::vector<DayStats> days = aggregate_days(records);
|
||||||
|
double agg_time = MPI_Wtime() - agg_start;
|
||||||
|
|
||||||
if (rank == 0) {
|
std::cout << "Rank " << rank
|
||||||
std::cout << "Rank 0 loading CSV..." << std::endl;
|
<< ": aggregated " << days.size() << " days"
|
||||||
|
<< " [" << (days.empty() ? 0 : days.front().day)
|
||||||
// Запускаем из build
|
<< ".." << (days.empty() ? 0 : days.back().day) << "]"
|
||||||
auto records = load_csv("../data/data.csv");
|
<< " in " << std::fixed << std::setprecision(3) << agg_time << " sec"
|
||||||
|
<< std::endl;
|
||||||
auto days = group_by_day(records);
|
|
||||||
auto parts = split_days(days, size);
|
|
||||||
|
|
||||||
// Рассылаем данные
|
|
||||||
for (int r = 0; r < size; r++) {
|
|
||||||
auto vec = select_records_for_rank(days, parts[r]);
|
|
||||||
|
|
||||||
if (r == 0) {
|
|
||||||
// себе не отправляем — сразу сохраняем
|
|
||||||
local_records = vec;
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
|
|
||||||
int count = static_cast<int>(vec.size());
|
|
||||||
MPI_Send(&count, 1, MPI_INT, r, 0, MPI_COMM_WORLD);
|
|
||||||
MPI_Send(vec.data(), count * sizeof(Record), MPI_BYTE, r, 1, MPI_COMM_WORLD);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
else {
|
|
||||||
// Принимает данные
|
|
||||||
int count = 0;
|
|
||||||
MPI_Recv(&count, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
|
|
||||||
|
|
||||||
local_records.resize(count);
|
|
||||||
MPI_Recv(local_records.data(), count * sizeof(Record),
|
|
||||||
MPI_BYTE, 0, 1, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
|
|
||||||
}
|
|
||||||
|
|
||||||
MPI_Barrier(MPI_COMM_WORLD);
|
|
||||||
|
|
||||||
std::cout << "Rank " << rank << " received "
|
|
||||||
<< local_records.size() << " records" << std::endl;
|
|
||||||
|
|
||||||
// ====== АГРЕГАЦИЯ НА КАЖДОМ УЗЛЕ ======
|
|
||||||
std::vector<DayStats> local_stats;
|
|
||||||
double time_start = omp_get_wtime();
|
|
||||||
|
|
||||||
// Время работы: [0] = GPU (если есть), [1..n] = CPU потоки
|
|
||||||
std::vector<double> worker_times(num_cpu_threads + 1, 0.0);
|
|
||||||
|
|
||||||
if (have_gpu && gpu_aggregate) {
|
|
||||||
// GPU узел: делим на (1 + num_cpu_threads) частей
|
|
||||||
int n_workers = 1 + num_cpu_threads;
|
|
||||||
auto parts = split_records(local_records, n_workers);
|
|
||||||
|
|
||||||
std::vector<std::vector<DayStats>> results(n_workers);
|
|
||||||
std::vector<bool> success(n_workers, true);
|
|
||||||
|
|
||||||
#pragma omp parallel
|
|
||||||
{
|
|
||||||
int tid = omp_get_thread_num();
|
|
||||||
if (tid < n_workers) {
|
|
||||||
double t0 = omp_get_wtime();
|
|
||||||
if (tid == 0) {
|
|
||||||
// GPU поток
|
|
||||||
success[0] = aggregate_days_gpu(parts[0], results[0], gpu_aggregate);
|
|
||||||
} else {
|
|
||||||
// CPU потоки
|
|
||||||
results[tid] = aggregate_days(parts[tid]);
|
|
||||||
}
|
|
||||||
worker_times[tid] = omp_get_wtime() - t0;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Объединяем результаты
|
|
||||||
for (int i = 0; i < n_workers; i++) {
|
|
||||||
if (i == 0 && !success[0]) {
|
|
||||||
// GPU failed - обработаем на CPU
|
|
||||||
std::cout << "Rank " << rank << ": GPU failed, processing on CPU" << std::endl;
|
|
||||||
double t0 = omp_get_wtime();
|
|
||||||
results[0] = aggregate_days(parts[0]);
|
|
||||||
worker_times[0] = omp_get_wtime() - t0;
|
|
||||||
}
|
|
||||||
local_stats.insert(local_stats.end(), results[i].begin(), results[i].end());
|
|
||||||
}
|
|
||||||
|
|
||||||
} else {
|
|
||||||
// CPU-only узел
|
|
||||||
auto parts = split_records(local_records, num_cpu_threads);
|
|
||||||
std::vector<std::vector<DayStats>> results(num_cpu_threads);
|
|
||||||
|
|
||||||
#pragma omp parallel
|
|
||||||
{
|
|
||||||
int tid = omp_get_thread_num();
|
|
||||||
if (tid < num_cpu_threads) {
|
|
||||||
double t0 = omp_get_wtime();
|
|
||||||
results[tid] = aggregate_days(parts[tid]);
|
|
||||||
worker_times[tid + 1] = omp_get_wtime() - t0; // +1 т.к. [0] для GPU
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
for (int i = 0; i < num_cpu_threads; i++) {
|
|
||||||
local_stats.insert(local_stats.end(), results[i].begin(), results[i].end());
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
double time_total = omp_get_wtime() - time_start;
|
|
||||||
|
|
||||||
// Вывод времени
|
|
||||||
std::cout << std::fixed << std::setprecision(3);
|
|
||||||
std::cout << "Rank " << rank << " aggregated " << local_stats.size() << " days in "
|
|
||||||
<< time_total << "s (";
|
|
||||||
if (have_gpu) {
|
|
||||||
std::cout << "GPU: " << worker_times[0] << "s, ";
|
|
||||||
}
|
|
||||||
for (int i = 0; i < num_cpu_threads; i++) {
|
|
||||||
int idx = have_gpu ? (i + 1) : (i + 1);
|
|
||||||
std::cout << "CPU" << i << ": " << worker_times[idx] << "s";
|
|
||||||
if (i < num_cpu_threads - 1) std::cout << ", ";
|
|
||||||
}
|
|
||||||
std::cout << ")" << std::endl;
|
|
||||||
|
|
||||||
// ====== СБОР АГРЕГИРОВАННЫХ ДАННЫХ НА RANK 0 ======
|
|
||||||
std::vector<DayStats> all_stats;
|
|
||||||
|
|
||||||
if (rank == 0) {
|
|
||||||
// Добавляем свои данные
|
|
||||||
all_stats.insert(all_stats.end(), local_stats.begin(), local_stats.end());
|
|
||||||
|
|
||||||
// Получаем данные от других узлов
|
|
||||||
for (int r = 1; r < size; r++) {
|
|
||||||
int count = 0;
|
|
||||||
MPI_Recv(&count, 1, MPI_INT, r, 2, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
|
|
||||||
|
|
||||||
std::vector<DayStats> remote_stats(count);
|
|
||||||
MPI_Recv(remote_stats.data(), count * sizeof(DayStats),
|
|
||||||
MPI_BYTE, r, 3, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
|
|
||||||
|
|
||||||
all_stats.insert(all_stats.end(), remote_stats.begin(), remote_stats.end());
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
// Отправляем свои агрегированные данные на rank 0
|
|
||||||
int count = static_cast<int>(local_stats.size());
|
|
||||||
MPI_Send(&count, 1, MPI_INT, 0, 2, MPI_COMM_WORLD);
|
|
||||||
MPI_Send(local_stats.data(), count * sizeof(DayStats), MPI_BYTE, 0, 3, MPI_COMM_WORLD);
|
|
||||||
}
|
|
||||||
|
|
||||||
// ====== ВЫЧИСЛЕНИЕ ИНТЕРВАЛОВ НА RANK 0 ======
|
|
||||||
if (rank == 0) {
|
|
||||||
std::cout << "Rank 0: merging " << all_stats.size() << " day stats..." << std::endl;
|
|
||||||
|
|
||||||
// Объединяем и сортируем
|
|
||||||
auto merged_stats = merge_day_stats(all_stats);
|
|
||||||
std::cout << "Rank 0: total " << merged_stats.size() << " unique days" << std::endl;
|
|
||||||
|
|
||||||
// Вычисляем интервалы
|
|
||||||
auto intervals = find_intervals(merged_stats, 0.10);
|
|
||||||
std::cout << "Found " << intervals.size() << " intervals with >=10% change" << std::endl;
|
|
||||||
|
|
||||||
// Записываем результат
|
|
||||||
write_intervals("../result.csv", intervals);
|
|
||||||
std::cout << "Results written to result.csv" << std::endl;
|
|
||||||
|
|
||||||
// Выводим первые несколько интервалов
|
|
||||||
std::cout << "\nFirst 5 intervals:\n";
|
|
||||||
std::cout << "start_date,end_date,min_open,max_close,change\n";
|
|
||||||
for (size_t i = 0; i < std::min(intervals.size(), size_t(5)); i++) {
|
|
||||||
const auto& iv = intervals[i];
|
|
||||||
std::cout << day_index_to_date(iv.start_day) << ","
|
|
||||||
<< day_index_to_date(iv.end_day) << ","
|
|
||||||
<< iv.min_open << ","
|
|
||||||
<< iv.max_close << ","
|
|
||||||
<< iv.change << "\n";
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
MPI_Finalize();
|
MPI_Finalize();
|
||||||
return 0;
|
return 0;
|
||||||
|
|||||||
@@ -1,11 +0,0 @@
|
|||||||
#include "mpi_utils.hpp"
|
|
||||||
#include <mpi.h>
|
|
||||||
#include <iostream>
|
|
||||||
|
|
||||||
void mpi_print_basic() {
|
|
||||||
int rank, size;
|
|
||||||
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
|
|
||||||
MPI_Comm_size(MPI_COMM_WORLD, &size);
|
|
||||||
|
|
||||||
std::cout << "Hello from rank " << rank << " of " << size << std::endl;
|
|
||||||
}
|
|
||||||
@@ -1,2 +0,0 @@
|
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#pragma once
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void mpi_print_basic();
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@@ -1,4 +1,8 @@
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#include "utils.hpp"
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#include "utils.hpp"
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#include <fstream>
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#include <sstream>
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#include <stdexcept>
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#include <numeric>
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std::map<DayIndex, std::vector<Record>> group_by_day(const std::vector<Record>& recs) {
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std::map<DayIndex, std::vector<Record>> group_by_day(const std::vector<Record>& recs) {
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std::map<DayIndex, std::vector<Record>> days;
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std::map<DayIndex, std::vector<Record>> days;
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@@ -23,3 +27,88 @@ std::vector<std::vector<DayIndex>> split_days(const std::map<DayIndex, std::vect
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return out;
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return out;
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}
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}
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int get_num_cpu_threads() {
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const char* env_threads = std::getenv("NUM_CPU_THREADS");
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int num_cpu_threads = 1;
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if (env_threads) {
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num_cpu_threads = std::atoi(env_threads);
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if (num_cpu_threads < 1) num_cpu_threads = 1;
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}
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return num_cpu_threads;
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}
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std::string get_env(const char* name) {
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const char* env = std::getenv(name);
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if (!env) {
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throw std::runtime_error(std::string("Environment variable not set: ") + name);
|
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}
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return std::string(env);
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||||||
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}
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||||||
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std::string get_data_path() {
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return get_env("DATA_PATH");
|
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}
|
||||||
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|
||||||
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std::vector<int> get_data_read_shares() {
|
||||||
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std::vector<int> shares;
|
||||||
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std::stringstream ss(get_env("DATA_READ_SHARES"));
|
||||||
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std::string item;
|
||||||
|
while (std::getline(ss, item, ',')) {
|
||||||
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shares.push_back(std::stoi(item));
|
||||||
|
}
|
||||||
|
return shares;
|
||||||
|
}
|
||||||
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|
||||||
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int64_t get_read_overlap_bytes() {
|
||||||
|
return std::stoll(get_env("READ_OVERLAP_BYTES"));
|
||||||
|
}
|
||||||
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|
||||||
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int64_t get_file_size(const std::string& path) {
|
||||||
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std::ifstream file(path, std::ios::binary | std::ios::ate);
|
||||||
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if (!file.is_open()) {
|
||||||
|
throw std::runtime_error("Cannot open file: " + path);
|
||||||
|
}
|
||||||
|
return static_cast<int64_t>(file.tellg());
|
||||||
|
}
|
||||||
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|
||||||
|
ByteRange calculate_byte_range(int rank, int size, int64_t file_size,
|
||||||
|
const std::vector<int>& shares, int64_t overlap_bytes) {
|
||||||
|
// Если shares пустой или не соответствует size, используем равные доли
|
||||||
|
std::vector<int> effective_shares;
|
||||||
|
if (shares.size() == static_cast<size_t>(size)) {
|
||||||
|
effective_shares = shares;
|
||||||
|
} else {
|
||||||
|
effective_shares.assign(size, 1);
|
||||||
|
}
|
||||||
|
|
||||||
|
int total_shares = std::accumulate(effective_shares.begin(), effective_shares.end(), 0);
|
||||||
|
|
||||||
|
// Вычисляем базовые границы для каждого ранка
|
||||||
|
int64_t bytes_per_share = file_size / total_shares;
|
||||||
|
|
||||||
|
int64_t base_start = 0;
|
||||||
|
for (int i = 0; i < rank; i++) {
|
||||||
|
base_start += bytes_per_share * effective_shares[i];
|
||||||
|
}
|
||||||
|
|
||||||
|
int64_t base_end = base_start + bytes_per_share * effective_shares[rank];
|
||||||
|
|
||||||
|
// Применяем overlap
|
||||||
|
ByteRange range;
|
||||||
|
|
||||||
|
if (rank == 0) {
|
||||||
|
// Первый ранк: начинаем с 0, добавляем overlap в конце
|
||||||
|
range.start = 0;
|
||||||
|
range.end = std::min(base_end + overlap_bytes, file_size);
|
||||||
|
} else if (rank == size - 1) {
|
||||||
|
// Последний ранк: вычитаем overlap в начале, читаем до конца файла
|
||||||
|
range.start = std::max(base_start - overlap_bytes, static_cast<int64_t>(0));
|
||||||
|
range.end = file_size;
|
||||||
|
} else {
|
||||||
|
// Промежуточные ранки: overlap с обеих сторон
|
||||||
|
range.start = std::max(base_start - overlap_bytes, static_cast<int64_t>(0));
|
||||||
|
range.end = std::min(base_end + overlap_bytes, file_size);
|
||||||
|
}
|
||||||
|
|
||||||
|
return range;
|
||||||
|
}
|
||||||
|
|||||||
@@ -4,6 +4,29 @@
|
|||||||
#include "day_stats.hpp"
|
#include "day_stats.hpp"
|
||||||
#include <map>
|
#include <map>
|
||||||
#include <vector>
|
#include <vector>
|
||||||
|
#include <string>
|
||||||
|
#include <cstdlib>
|
||||||
|
#include <cstdint>
|
||||||
|
|
||||||
|
// Группировка записей по дням
|
||||||
std::map<DayIndex, std::vector<Record>> group_by_day(const std::vector<Record>& recs);
|
std::map<DayIndex, std::vector<Record>> group_by_day(const std::vector<Record>& recs);
|
||||||
std::vector<std::vector<DayIndex>> split_days(const std::map<DayIndex, std::vector<Record>>& days, int parts);
|
std::vector<std::vector<DayIndex>> split_days(const std::map<DayIndex, std::vector<Record>>& days, int parts);
|
||||||
|
|
||||||
|
// Чтение переменных окружения
|
||||||
|
int get_num_cpu_threads();
|
||||||
|
std::string get_data_path();
|
||||||
|
std::vector<int> get_data_read_shares();
|
||||||
|
int64_t get_read_overlap_bytes();
|
||||||
|
|
||||||
|
// Структура для хранения диапазона байт для чтения
|
||||||
|
struct ByteRange {
|
||||||
|
int64_t start;
|
||||||
|
int64_t end; // exclusive
|
||||||
|
};
|
||||||
|
|
||||||
|
// Вычисляет диапазон байт для конкретного ранка
|
||||||
|
ByteRange calculate_byte_range(int rank, int size, int64_t file_size,
|
||||||
|
const std::vector<int>& shares, int64_t overlap_bytes);
|
||||||
|
|
||||||
|
// Получение размера файла
|
||||||
|
int64_t get_file_size(const std::string& path);
|
||||||
|
|||||||
Reference in New Issue
Block a user