univer-7th-semester/supercomputers
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Author SHA1 Message Date
Arity-T
ab18d9770f Агрегация 2025-12-13 12:45:29 +00:00
Arity-T
6a22dc3ef7 Параллельное чтение данных 2025-12-13 12:13:23 +00:00
Arity-T
f90a641754 gpu_is_available 2025-12-13 11:07:31 +00:00
Arity-T
10bd6db2b8 Замечание про NFS 2025-12-13 11:06:41 +00:00
Arity-T
d82fde7116 Уточнил задание 2025-12-11 16:47:30 +03:00
Arity-T
7f16a5c17a Больше таймеров 2025-12-11 10:08:22 +00:00
Arity-T
44f297e55a Удалил бесполезные файлы 2025-12-11 09:06:48 +00:00
19 changed files with 778 additions and 645 deletions
Expand all files Collapse all files

2
.gitignore vendored
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@@ -1,4 +1,4 @@
data
build
out.txt
result.csv
*.csv

22
README.md
View File

@@ -10,10 +10,30 @@
не менее чем на 10% от даты начала интервала, вместе с минимальными и максимальными
значениями Open и Close за все дни внутри интервала.
## Параллельное чтение данных
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
на NFS сервер, который уже читает реальные данные с диска и лишь затем отправляет
их другим узлам.
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
sudo chown $USER /data
# Копируем данные
cd /mnt/shared/supercomputers/data
cp data.csv /data/
```
## Сборка
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`.
```sh

445
data.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 20,
"id": "2acce44b",
"metadata": {},
"outputs": [],
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 21,
"id": "5ba70af7",
"metadata": {},
"outputs": [
@@ -111,7 +111,7 @@
"7317758 0.410369 "
]
},
"execution_count": 14,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -124,8 +124,8 @@
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d4b22f3b",
"execution_count": 23,
"id": "3b320537",
"metadata": {},
"outputs": [
{
@@ -150,67 +150,190 @@
" <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>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</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",
" <th>7317754</th>\n",
" <td>2025-11-30 23:55:00+00:00</td>\n",
" <td>90405.0</td>\n",
" <td>90452.0</td>\n",
" <td>90403.0</td>\n",
" <td>90452.0</td>\n",
" <td>0.531700</td>\n",
" <td>90427.5</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",
" <th>7317755</th>\n",
" <td>2025-11-30 23:56:00+00:00</td>\n",
" <td>90452.0</td>\n",
" <td>90481.0</td>\n",
" <td>90420.0</td>\n",
" <td>90420.0</td>\n",
" <td>0.055547</td>\n",
" <td>90450.5</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",
" <th>7317756</th>\n",
" <td>2025-11-30 23:57:00+00:00</td>\n",
" <td>90412.0</td>\n",
" <td>90458.0</td>\n",
" <td>90396.0</td>\n",
" <td>90435.0</td>\n",
" <td>0.301931</td>\n",
" <td>90427.0</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",
" </tr>\n",
" <tr>\n",
" <th>5082</th>\n",
" <td>2025-11-30</td>\n",
" <th>7317757</th>\n",
" <td>2025-11-30 23:58:00+00:00</td>\n",
" <td>90428.0</td>\n",
" <td>90428.0</td>\n",
" <td>90362.0</td>\n",
" <td>90362.0</td>\n",
" <td>4.591653</td>\n",
" <td>90395.0</td>\n",
" </tr>\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>91969.0</td>\n",
" <td>90832.0</td>\n",
" <td>90382.0</td>\n",
" <td>0.410369</td>\n",
" <td>90374.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Timestamp Low High Open Close\n",
"5078 2025-11-26 86304.0 90646.0 87331.0 90477.0\n",
"5079 2025-11-27 90091.0 91926.0 90476.0 91325.0\n",
"5080 2025-11-28 90233.0 93091.0 91326.0 90913.0\n",
"5081 2025-11-29 90216.0 91179.0 90913.0 90832.0\n",
"5082 2025-11-30 90362.0 91969.0 90832.0 90382.0"
" Timestamp Open High Low Close \\\n",
"7317754 2025-11-30 23:55:00+00:00 90405.0 90452.0 90403.0 90452.0 \n",
"7317755 2025-11-30 23:56:00+00:00 90452.0 90481.0 90420.0 90420.0 \n",
"7317756 2025-11-30 23:57:00+00:00 90412.0 90458.0 90396.0 90435.0 \n",
"7317757 2025-11-30 23:58:00+00:00 90428.0 90428.0 90362.0 90362.0 \n",
"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": {
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],
"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": {},
"output_type": "execute_result"
}
@@ -218,7 +341,13 @@
"source": [
"df_days = (\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",
")\n",
"df_days.tail()"
@@ -226,111 +355,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"id": "91823496",
"metadata": {},
"outputs": [
{
"data": {
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" <th>5081</th>\n",
" <td>2025-11-29</td>\n",
" <td>90216.0</td>\n",
" <td>91179.0</td>\n",
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" <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,
"execution_count": 26,
"id": "9a7b3310",
"metadata": {},
"outputs": [
@@ -358,6 +383,8 @@
" <th>start_date</th>\n",
" <th>end_date</th>\n",
" <th>min_open</th>\n",
" <th>max_open</th>\n",
" <th>min_close</th>\n",
" <th>max_close</th>\n",
" <th>start_avg</th>\n",
" <th>end_avg</th>\n",
@@ -366,76 +393,86 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>335</th>\n",
" <th>316</th>\n",
" <td>2025-02-27</td>\n",
" <td>2025-04-23</td>\n",
" <td>76252.0</td>\n",
" <td>94273.0</td>\n",
" <td>84801.5</td>\n",
" <td>93335.0</td>\n",
" <td>0.100629</td>\n",
" <td>2025-04-25</td>\n",
" <td>74509.0</td>\n",
" <td>95801.0</td>\n",
" <td>74515.0</td>\n",
" <td>95800.0</td>\n",
" <td>85166.063889</td>\n",
" <td>94303.907292</td>\n",
" <td>0.107294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>2025-04-24</td>\n",
" <td>2025-05-09</td>\n",
" <td>93730.0</td>\n",
" <td>103261.0</td>\n",
" <td>92867.5</td>\n",
" <td>103341.0</td>\n",
" <td>0.112779</td>\n",
" <th>317</th>\n",
" <td>2025-04-26</td>\n",
" <td>2025-05-11</td>\n",
" <td>92877.0</td>\n",
" <td>104971.0</td>\n",
" <td>92872.0</td>\n",
" <td>104965.0</td>\n",
" <td>94500.950347</td>\n",
" <td>104182.167708</td>\n",
" <td>0.102446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>2025-05-10</td>\n",
" <th>318</th>\n",
" <td>2025-05-12</td>\n",
" <td>2025-07-11</td>\n",
" <td>100990.0</td>\n",
" <td>117579.0</td>\n",
" <td>103915.0</td>\n",
" <td>117032.5</td>\n",
" <td>0.126233</td>\n",
" <td>98384.0</td>\n",
" <td>118833.0</td>\n",
" <td>98382.0</td>\n",
" <td>118839.0</td>\n",
" <td>103569.791319</td>\n",
" <td>117463.666667</td>\n",
" <td>0.134150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338</th>\n",
" <th>319</th>\n",
" <td>2025-07-12</td>\n",
" <td>2025-11-04</td>\n",
" <td>106470.0</td>\n",
" <td>124728.0</td>\n",
" <td>117599.0</td>\n",
" <td>103079.0</td>\n",
" <td>0.123470</td>\n",
" <td>98944.0</td>\n",
" <td>126202.0</td>\n",
" <td>98943.0</td>\n",
" <td>126202.0</td>\n",
" <td>117640.026389</td>\n",
" <td>103712.985764</td>\n",
" <td>0.118387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <th>320</th>\n",
" <td>2025-11-05</td>\n",
" <td>2025-11-18</td>\n",
" <td>92112.0</td>\n",
" <td>105972.0</td>\n",
" <td>101737.5</td>\n",
" <td>91471.0</td>\n",
" <td>0.100912</td>\n",
" <td>89291.0</td>\n",
" <td>107343.0</td>\n",
" <td>89286.0</td>\n",
" <td>107343.0</td>\n",
" <td>102514.621181</td>\n",
" <td>91705.833333</td>\n",
" <td>0.105437</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_date end_date min_open max_close start_avg end_avg \\\n",
"335 2025-02-27 2025-04-23 76252.0 94273.0 84801.5 93335.0 \n",
"336 2025-04-24 2025-05-09 93730.0 103261.0 92867.5 103341.0 \n",
"337 2025-05-10 2025-07-11 100990.0 117579.0 103915.0 117032.5 \n",
"338 2025-07-12 2025-11-04 106470.0 124728.0 117599.0 103079.0 \n",
"339 2025-11-05 2025-11-18 92112.0 105972.0 101737.5 91471.0 \n",
" start_date end_date min_open max_open min_close max_close \\\n",
"316 2025-02-27 2025-04-25 74509.0 95801.0 74515.0 95800.0 \n",
"317 2025-04-26 2025-05-11 92877.0 104971.0 92872.0 104965.0 \n",
"318 2025-05-12 2025-07-11 98384.0 118833.0 98382.0 118839.0 \n",
"319 2025-07-12 2025-11-04 98944.0 126202.0 98943.0 126202.0 \n",
"320 2025-11-05 2025-11-18 89291.0 107343.0 89286.0 107343.0 \n",
"\n",
" change \n",
"335 0.100629 \n",
"336 0.112779 \n",
"337 0.126233 \n",
"338 0.123470 \n",
"339 0.100912 "
" start_avg end_avg change \n",
"316 85166.063889 94303.907292 0.107294 \n",
"317 94500.950347 104182.167708 0.102446 \n",
"318 103569.791319 117463.666667 0.134150 \n",
"319 117640.026389 103712.985764 0.118387 \n",
"320 102514.621181 91705.833333 0.105437 "
]
},
"execution_count": 25,
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -455,8 +492,10 @@
" intervals.append({\n",
" \"start_date\": df_days.loc[start_idx, \"Timestamp\"],\n",
" \"end_date\": df_days.loc[i, \"Timestamp\"],\n",
" \"min_open\": interval[\"Open\"].min(),\n",
" \"max_close\": interval[\"Close\"].max(),\n",
" \"min_open\": interval[\"OpenMin\"].min(),\n",
" \"max_open\": interval[\"OpenMax\"].max(),\n",
" \"min_close\": interval[\"CloseMin\"].min(),\n",
" \"max_close\": interval[\"CloseMax\"].max(),\n",
" \"start_avg\": price_base,\n",
" \"end_avg\": price_now,\n",
" \"change\": change,\n",
@@ -470,6 +509,14 @@
"df_intervals = pd.DataFrame(intervals)\n",
"df_intervals.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07f1cd58",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

10
run.slurm
View File

@@ -5,8 +5,14 @@
#SBATCH --cpus-per-task=2
#SBATCH --output=out.txt
# Количество CPU потоков на узел (должно соответствовать cpus-per-task)
export NUM_CPU_THREADS=2
# Путь к файлу данных (должен существовать на всех узлах)
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
mpirun -np $SLURM_NTASKS ./bitcoin_app

97
src/aggregation.cpp
View File

@@ -1,87 +1,48 @@
#include "aggregation.hpp"
#include <map>
#include <algorithm>
#include <limits>
#include <cmath>
std::vector<DayStats> aggregate_days(const std::vector<Record>& records) {
// Группируем записи по дням
std::map<DayIndex, std::vector<const Record*>> day_records;
// Накопители для каждого дня
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) {
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;
result.reserve(day_records.size());
for (auto& [day, recs] : day_records) {
// Сортируем по timestamp для определения first/last
std::sort(recs.begin(), recs.end(),
[](const Record* a, const Record* b) {
return a->timestamp < b->timestamp;
});
result.reserve(days.size());
for (const auto& [day, acc] : days) {
DayStats stats;
stats.day = day;
stats.low = std::numeric_limits<double>::max();
stats.high = std::numeric_limits<double>::lowest();
stats.open = recs.front()->open;
stats.close = recs.back()->close;
stats.first_ts = recs.front()->timestamp;
stats.last_ts = recs.back()->timestamp;
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;
stats.avg = acc.avg_sum / static_cast<double>(acc.count);
stats.open_min = acc.open_min;
stats.open_max = acc.open_max;
stats.close_min = acc.close_min;
stats.close_max = acc.close_max;
stats.count = acc.count;
result.push_back(stats);
}
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;
}

6
src/aggregation.hpp
View File

@@ -3,12 +3,6 @@
#include "record.hpp"
#include "day_stats.hpp"
#include <vector>
#include <map>
// Агрегация записей по дням на одном узле
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);

140
src/csv_loader.cpp
View File

@@ -2,45 +2,133 @@
#include <fstream>
#include <sstream>
#include <iostream>
#include <stdexcept>
std::vector<Record> load_csv(const std::string& filename) {
std::vector<Record> data;
std::ifstream file(filename);
if (!file.is_open()) {
throw std::runtime_error("Cannot open file: " + filename);
bool parse_csv_line(const std::string& line, Record& record) {
if (line.empty()) {
return false;
}
std::string line;
// читаем первую строку (заголовок)
std::getline(file, line);
while (std::getline(file, line)) {
std::stringstream ss(line);
std::string item;
Record row;
try {
// timestamp
if (!std::getline(ss, item, ',') || item.empty()) return false;
record.timestamp = std::stod(item);
std::getline(ss, item, ',');
row.timestamp = std::stod(item);
// open
if (!std::getline(ss, item, ',') || item.empty()) return false;
record.open = std::stod(item);
std::getline(ss, item, ',');
row.open = std::stod(item);
// high
if (!std::getline(ss, item, ',') || item.empty()) return false;
record.high = std::stod(item);
std::getline(ss, item, ',');
row.high = std::stod(item);
// low
if (!std::getline(ss, item, ',') || item.empty()) return false;
record.low = std::stod(item);
std::getline(ss, item, ',');
row.low = std::stod(item);
// close
if (!std::getline(ss, item, ',') || item.empty()) return false;
record.close = std::stod(item);
std::getline(ss, item, ',');
row.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);
}
std::getline(ss, item, ',');
row.volume = std::stod(item);
return true;
} catch (const std::exception&) {
return false;
}
}
data.push_back(row);
std::vector<Record> load_csv_parallel(int rank, int size) {
std::vector<Record> data;
// 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()) {
throw std::runtime_error("Cannot open file: " + data_path);
}
// Переходим к началу диапазона
file.seekg(range.start);
// Читаем данные в буфер
int64_t bytes_to_read = range.end - range.start;
std::vector<char> buffer(bytes_to_read);
file.read(buffer.data(), bytes_to_read);
int64_t bytes_read = file.gcount();
file.close();
// Преобразуем в строку для удобства парсинга
std::string content(buffer.data(), bytes_read);
// Находим позицию начала первой полной строки
size_t parse_start = 0;
if (rank == 0) {
// Первый ранк: пропускаем заголовок (первую строку)
size_t header_end = content.find('\n');
if (header_end != std::string::npos) {
parse_start = header_end + 1;
}
} else {
// Остальные ранки: начинаем с первого \n (пропускаем неполную строку)
size_t first_newline = content.find('\n');
if (first_newline != std::string::npos) {
parse_start = first_newline + 1;
}
}
// 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;

11
src/csv_loader.hpp
View File

@@ -2,5 +2,14 @@
#include <string>
#include <vector>
#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);

27
src/day_stats.hpp
View File

@@ -1,28 +1,15 @@
#pragma once
#include <cstdint>
using DayIndex = long long;
using DayIndex = int64_t;
// Агрегированные данные за один день
struct DayStats {
DayIndex day; // индекс дня (timestamp / 86400)
double low; // минимальный Low за день
double high; // максимальный High за день
double open; // первый Open за день
double close; // последний Close за день
double avg; // среднее = (low + high) / 2
double first_ts; // timestamp первой записи (для определения порядка open)
double last_ts; // timestamp последней записи (для определения close)
double avg; // среднее значение (Low + High) / 2 по всем записям
double open_min; // минимальный Open за день
double open_max; // максимальный Open за день
double close_min; // минимальный Close за день
double close_max; // максимальный Close за день
int64_t count; // количество записей, по которым агрегировали
};
// 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;
};

43
src/gpu_loader.cpp
View File

@@ -2,6 +2,9 @@
#include <dlfcn.h>
#include <map>
#include <algorithm>
#include <iostream>
#include <iomanip>
#include <omp.h>
static void* get_gpu_lib_handle() {
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;
}
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() {
void* h = get_gpu_lib_handle();
if (!h) return nullptr;
@@ -33,6 +46,12 @@ bool aggregate_days_gpu(
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
std::map<DayIndex, std::vector<size_t>> day_record_indices;
@@ -80,7 +99,12 @@ bool aggregate_days_gpu(
// Выделяем память для результата
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(
gpu_records.data(),
static_cast<int>(gpu_records.size()),
@@ -92,6 +116,7 @@ bool aggregate_days_gpu(
);
if (result != 0) {
std::cout << " GPU: Function returned error code " << result << std::endl;
return false;
}
@@ -102,15 +127,19 @@ bool aggregate_days_gpu(
for (const auto& gs : gpu_stats) {
DayStats ds;
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.first_ts = gs.first_ts;
ds.last_ts = gs.last_ts;
ds.open_min = gs.open_min;
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);
}
// 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;
}

13
src/gpu_loader.hpp
View File

@@ -3,6 +3,8 @@
#include "record.hpp"
#include <vector>
bool gpu_is_available();
// Типы функций из GPU плагина
using gpu_is_available_fn = int (*)();
@@ -18,13 +20,12 @@ struct GpuRecord {
struct GpuDayStats {
long long day;
double low;
double high;
double open;
double close;
double avg;
double first_ts;
double last_ts;
double open_min;
double open_max;
double close_min;
double close_max;
long long count;
};
using gpu_aggregate_days_fn = int (*)(

172
src/gpu_plugin.cu
View File

@@ -1,6 +1,15 @@
#include <cuda_runtime.h>
#include <cstdint>
#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)
struct GpuRecord {
@@ -14,19 +23,22 @@ struct GpuRecord {
struct GpuDayStats {
long long day;
double low;
double high;
double open;
double close;
double avg;
double first_ts;
double last_ts;
double open_min;
double open_max;
double close_min;
double close_max;
long long count;
};
extern "C" int gpu_is_available() {
int n = 0;
cudaError_t err = cudaGetDeviceCount(&n);
if (err != cudaSuccess) return 0;
if (n > 0) {
// Инициализируем CUDA контекст заранее (cudaFree(0) форсирует инициализацию)
cudaFree(0);
}
return (n > 0) ? 1 : 0;
}
@@ -50,32 +62,30 @@ __global__ void aggregate_kernel(
GpuDayStats stats;
stats.day = day_indices[d];
stats.low = DBL_MAX;
stats.high = -DBL_MAX;
stats.first_ts = DBL_MAX;
stats.last_ts = -DBL_MAX;
stats.open = 0;
stats.close = 0;
stats.open_min = DBL_MAX;
stats.open_max = -DBL_MAX;
stats.close_min = DBL_MAX;
stats.close_max = -DBL_MAX;
stats.count = count;
double avg_sum = 0.0;
for (int i = 0; i < count; i++) {
const GpuRecord& r = records[offset + i];
// min/max
if (r.low < stats.low) stats.low = r.low;
if (r.high > stats.high) stats.high = r.high;
// Accumulate avg = (low + high) / 2
avg_sum += (r.low + r.high) / 2.0;
// first/last по timestamp
if (r.timestamp < stats.first_ts) {
stats.first_ts = r.timestamp;
stats.open = r.open;
}
if (r.timestamp > stats.last_ts) {
stats.last_ts = r.timestamp;
stats.close = r.close;
}
// min/max Open
if (r.open < stats.open_min) stats.open_min = r.open;
if (r.open > stats.open_max) stats.open_max = r.open;
// min/max Close
if (r.close < stats.close_min) stats.close_min = r.close;
if (r.close > stats.close_max) stats.close_max = r.close;
}
stats.avg = (stats.low + stats.high) / 2.0;
stats.avg = avg_sum / static_cast<double>(count);
out_stats[d] = stats;
}
@@ -89,7 +99,33 @@ extern "C" int gpu_aggregate_days(
int num_days,
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;
int* d_day_offsets = 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));
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);
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);
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;
int num_blocks = (num_days + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;
cudaEventRecord(start_kernel);
aggregate_kernel<<<num_blocks, THREADS_PER_BLOCK>>>(
d_records, num_records,
d_day_offsets, d_day_counts, d_day_indices,
num_days, d_out_stats
);
// Проверяем ошибку запуска kernel
err = cudaGetLastError();
if (err != cudaSuccess) {
cudaFree(d_records);
@@ -147,26 +198,65 @@ extern "C" int gpu_aggregate_days(
return -7;
}
// Ждём завершения
err = cudaDeviceSynchronize();
if (err != cudaSuccess) {
cudaFree(d_records);
cudaFree(d_day_offsets);
cudaFree(d_day_counts);
cudaFree(d_day_indices);
cudaFree(d_out_stats);
return -6;
}
cudaEventRecord(stop_kernel);
cudaEventSynchronize(stop_kernel);
// 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);
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_day_offsets);
cudaFree(d_day_counts);
cudaFree(d_day_indices);
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;
}

25
src/intervals.cpp
View File

@@ -28,13 +28,17 @@ std::vector<Interval> find_intervals(const std::vector<DayStats>& days, double t
interval.end_avg = price_now;
interval.change = change;
// Находим min(Open) и max(Close) в интервале
interval.min_open = days[start_idx].open;
interval.max_close = days[start_idx].close;
// Находим min/max Open и Close в интервале
interval.open_min = days[start_idx].open_min;
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++) {
interval.min_open = std::min(interval.min_open, days[j].open);
interval.max_close = std::max(interval.max_close, days[j].close);
for (size_t j = start_idx + 1; j <= i; j++) {
interval.open_min = std::min(interval.open_min, days[j].open_min);
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);
@@ -68,16 +72,17 @@ void write_intervals(const std::string& filename, const std::vector<Interval>& i
std::ofstream out(filename);
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) {
out << day_index_to_date(iv.start_day) << ","
<< day_index_to_date(iv.end_day) << ","
<< iv.min_open << ","
<< iv.max_close << ","
<< iv.open_min << ","
<< iv.open_max << ","
<< iv.close_min << ","
<< iv.close_max << ","
<< iv.start_avg << ","
<< iv.end_avg << ","
<< std::setprecision(6) << iv.change << "\n";
}
}

14
src/intervals.hpp
View File

@@ -4,6 +4,19 @@
#include <vector>
#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%)
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)
std::string day_index_to_date(DayIndex day);

251
src/main.cpp
View File

@@ -1,54 +1,12 @@
#include <mpi.h>
#include <omp.h>
#include <iostream>
#include <vector>
#include <map>
#include <iomanip>
#include <cstdlib>
#include "csv_loader.hpp"
#include "utils.hpp"
#include "record.hpp"
#include "day_stats.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) {
MPI_Init(&argc, &argv);
@@ -57,200 +15,27 @@ int main(int argc, char** argv) {
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
// Читаем количество CPU потоков из переменной окружения (по умолчанию 2)
int num_cpu_threads = 2;
const char* env_threads = std::getenv("NUM_CPU_THREADS");
if (env_threads) {
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
// Параллельное чтение данных
double read_start = MPI_Wtime();
std::vector<Record> records = load_csv_parallel(rank, size);
double read_time = MPI_Wtime() - read_start;
// ====== ЗАГРУЗКА GPU ФУНКЦИЙ ======
auto gpu_is_available = load_gpu_is_available();
auto gpu_aggregate = load_gpu_aggregate_days();
std::cout << "Rank " << rank
<< ": read " << records.size() << " records"
<< " 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;
}
// Агрегация по дням
double agg_start = MPI_Wtime();
std::vector<DayStats> days = aggregate_days(records);
double agg_time = MPI_Wtime() - agg_start;
std::vector<Record> local_records;
if (rank == 0) {
std::cout << "Rank 0 loading CSV..." << std::endl;
// Запускаем из build
auto records = load_csv("../data/data.csv");
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";
}
}
std::cout << "Rank " << rank
<< ": aggregated " << days.size() << " days"
<< " [" << (days.empty() ? 0 : days.front().day)
<< ".." << (days.empty() ? 0 : days.back().day) << "]"
<< " in " << std::fixed << std::setprecision(3) << agg_time << " sec"
<< std::endl;
MPI_Finalize();
return 0;

11
src/mpi_utils.cpp
View File

@@ -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;
}

2
src/mpi_utils.hpp
View File

@@ -1,2 +0,0 @@
#pragma once
void mpi_print_basic();

89
src/utils.cpp
View File

@@ -1,4 +1,8 @@
#include "utils.hpp"
#include <fstream>
#include <sstream>
#include <stdexcept>
#include <numeric>
std::map<DayIndex, std::vector<Record>> group_by_day(const std::vector<Record>& recs) {
std::map<DayIndex, std::vector<Record>> days;
@@ -23,3 +27,88 @@ std::vector<std::vector<DayIndex>> split_days(const std::map<DayIndex, std::vect
return out;
}
int get_num_cpu_threads() {
const char* env_threads = std::getenv("NUM_CPU_THREADS");
int num_cpu_threads = 1;
if (env_threads) {
num_cpu_threads = std::atoi(env_threads);
if (num_cpu_threads < 1) num_cpu_threads = 1;
}
return num_cpu_threads;
}
std::string get_env(const char* name) {
const char* env = std::getenv(name);
if (!env) {
throw std::runtime_error(std::string("Environment variable not set: ") + name);
}
return std::string(env);
}
std::string get_data_path() {
return get_env("DATA_PATH");
}
std::vector<int> get_data_read_shares() {
std::vector<int> shares;
std::stringstream ss(get_env("DATA_READ_SHARES"));
std::string item;
while (std::getline(ss, item, ',')) {
shares.push_back(std::stoi(item));
}
return shares;
}
int64_t get_read_overlap_bytes() {
return std::stoll(get_env("READ_OVERLAP_BYTES"));
}
int64_t get_file_size(const std::string& path) {
std::ifstream file(path, std::ios::binary | std::ios::ate);
if (!file.is_open()) {
throw std::runtime_error("Cannot open file: " + path);
}
return static_cast<int64_t>(file.tellg());
}
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;
}

23
src/utils.hpp
View File

@@ -4,6 +4,29 @@
#include "day_stats.hpp"
#include <map>
#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::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);