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[WIP] 重构样板赛题

This commit is contained in:
Kevin Zhang 2025-09-03 23:03:47 +08:00
parent ab43e17fc9
commit 79e4fd6ab1
14 changed files with 352 additions and 76 deletions
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.gitignore vendored Normal file
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.DS_Store
*.bak
*.pyc
*.o
*/build/
cp_template/*.yaml

49
README.md
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@ -32,7 +32,52 @@
---
## 📥 如何参与提交？
## 🚀 快速上手
本竞赛旨在评估参赛者在GPU并行计算领域的算法优化能力。为了快速让参赛者进入比赛状态，可选择实现三个核心算法的高性能版本：
- **ReduceSum**: 高精度归约求和
- **SortPair**: 键值对稳定排序
- **TopkPair**: 键值对TopK选择
### 📥
### 编译和测试
#### 1. 全量编译和运行
```bash
# 编译并运行所有算法测试（默认行为）
./run.sh
# 仅编译所有算法，不运行测试
./run.sh --build-only
# 编译并运行单个算法测试
./run.sh --run_reduce # ReduceSum算法
./run.sh --run_sort # SortPair算法
./run.sh --run_topk # TopkPair算法
```
#### 2. 单独编译和运行
```bash
# 编译并运行ReduceSum算法（默认行为）
./run_reduce_sum.sh
# 仅编译ReduceSum算法，不运行测试
./run_reduce_sum.sh --build-only
# 编译并运行SortPair正确性测试
./run_sort_pair.sh --run correctness
# 编译并运行TopkPair性能测试
./run_topk_pair.sh --run performance
```
#### 3. 手动运行测试
```bash
./build/test_reducesum [correctness|performance|all]
./build/test_sortpair [correctness|performance|all]
./build/test_topkpair [correctness|performance|all]
```
### ✅ 参赛要求：
- 提交内容必须可以在沐曦自研 GPU **曦云 C500** 上运行。
@ -72,7 +117,7 @@
## 🏅 排名规则
- 比赛周期：2 个月
- 比赛周期：2 个月
- 排名按累计得分排序，取前 12 名！
若得分相同：

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# ReduceSum算法性能测试结果
# 生成时间: 2025-09-03 22:34:18
algorithm: "ReduceSum"
data_types:
input: "float"
output: "float"
formulas:
throughput: "elements / time(s) / 1e9 (G/s)"
performance_data:
- data_size: 1000000
time_ms: 0.048717
throughput_gps: 20.526799
data_type: "float"
- data_size: 134217728
time_ms: 0.402560
throughput_gps: 333.410496
data_type: "float"
- data_size: 536870912
time_ms: 1.346586
throughput_gps: 398.690510
data_type: "float"
- data_size: 1073741824
time_ms: 2.639513
throughput_gps: 406.795353
data_type: "float"

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# SortPair算法性能测试结果
# 生成时间: 2025-09-03 22:37:18
algorithm: "SortPair"
data_types:
key_type: "float"
value_type: "uint32_t"
formulas:
throughput: "elements / time(s) / 1e9 (G/s)"
performance_data:
- data_size: 1000000
ascending:
time_ms: 0.351488
throughput_gps: 2.845047
descending:
time_ms: 0.343270
throughput_gps: 2.913155
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
ascending:
time_ms: 22.273815
throughput_gps: 6.025808
descending:
time_ms: 22.494003
throughput_gps: 5.966823
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
ascending:
time_ms: 88.856277
throughput_gps: 6.042014
descending:
time_ms: 89.913918
throughput_gps: 5.970943
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
ascending:
time_ms: 181.409576
throughput_gps: 5.918882
descending:
time_ms: 183.428955
throughput_gps: 5.853720
key_type: "float"
value_type: "uint32_t"

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S1/ICTN0N/topk_pair_performance.yaml Normal file
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# TopkPair算法性能测试结果
# 生成时间: 2025-09-03 22:40:54
algorithm: "TopkPair"
data_types:
key_type: "float"
value_type: "uint32_t"
formulas:
throughput: "elements / time(s) / 1e9 (G/s)"
performance_data:
- data_size: 1000000
k_value: 32
ascending:
time_ms: 0.402509
throughput_gps: 2.484418
descending:
time_ms: 0.416307
throughput_gps: 2.402072
key_type: "float"
value_type: "uint32_t"
- data_size: 1000000
k_value: 50
ascending:
time_ms: 0.404787
throughput_gps: 2.470434
descending:
time_ms: 0.414669
throughput_gps: 2.411563
key_type: "float"
value_type: "uint32_t"
- data_size: 1000000
k_value: 100
ascending:
time_ms: 0.398336
throughput_gps: 2.510443
descending:
time_ms: 0.408320
throughput_gps: 2.449060
key_type: "float"
value_type: "uint32_t"
- data_size: 1000000
k_value: 256
ascending:
time_ms: 0.410752
throughput_gps: 2.434559
descending:
time_ms: 0.403379
throughput_gps: 2.479057
key_type: "float"
value_type: "uint32_t"
- data_size: 1000000
k_value: 1024
ascending:
time_ms: 0.391091
throughput_gps: 2.556949
descending:
time_ms: 0.391142
throughput_gps: 2.556613
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
k_value: 32
ascending:
time_ms: 22.394062
throughput_gps: 5.993452
descending:
time_ms: 22.263729
throughput_gps: 6.028538
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
k_value: 50
ascending:
time_ms: 22.379187
throughput_gps: 5.997435
descending:
time_ms: 22.228352
throughput_gps: 6.038132
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
k_value: 100
ascending:
time_ms: 22.436581
throughput_gps: 5.982094
descending:
time_ms: 22.229326
throughput_gps: 6.037868
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
k_value: 256
ascending:
time_ms: 22.463232
throughput_gps: 5.974996
descending:
time_ms: 22.319946
throughput_gps: 6.013354
key_type: "float"
value_type: "uint32_t"
- data_size: 134217728
k_value: 1024
ascending:
time_ms: 22.468454
throughput_gps: 5.973608
descending:
time_ms: 22.335976
throughput_gps: 6.009038
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
k_value: 32
ascending:
time_ms: 89.437294
throughput_gps: 6.002763
descending:
time_ms: 88.605972
throughput_gps: 6.059083
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
k_value: 50
ascending:
time_ms: 89.460587
throughput_gps: 6.001200
descending:
time_ms: 88.546509
throughput_gps: 6.063152
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
k_value: 100
ascending:
time_ms: 89.203011
throughput_gps: 6.018529
descending:
time_ms: 88.809097
throughput_gps: 6.045224
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
k_value: 256
ascending:
time_ms: 89.500465
throughput_gps: 5.998526
descending:
time_ms: 88.743912
throughput_gps: 6.049665
key_type: "float"
value_type: "uint32_t"
- data_size: 536870912
k_value: 1024
ascending:
time_ms: 89.405357
throughput_gps: 6.004908
descending:
time_ms: 88.446083
throughput_gps: 6.070036
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
k_value: 32
ascending:
time_ms: 182.233307
throughput_gps: 5.892127
descending:
time_ms: 181.076950
throughput_gps: 5.929754
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
k_value: 50
ascending:
time_ms: 182.273239
throughput_gps: 5.890836
descending:
time_ms: 180.944550
throughput_gps: 5.934093
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
k_value: 100
ascending:
time_ms: 182.374191
throughput_gps: 5.887576
descending:
time_ms: 181.277100
throughput_gps: 5.923207
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
k_value: 256
ascending:
time_ms: 182.349457
throughput_gps: 5.888374
descending:
time_ms: 181.248199
throughput_gps: 5.924152
key_type: "float"
value_type: "uint32_t"
- data_size: 1073741824
k_value: 1024
ascending:
time_ms: 182.378326
throughput_gps: 5.887442
descending:
time_ms: 181.025803
throughput_gps: 5.931430
key_type: "float"
value_type: "uint32_t"

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# GPU 高性能并行计算算法优化竞赛
## 🎯 竞赛概述
本竞赛旨在评估参赛者在GPU并行计算领域的算法优化能力。参赛者可选择实现三个核心算法的高性能版本：
- **ReduceSum**: 高精度归约求和
- **SortPair**: 键值对稳定排序
- **TopkPair**: 键值对TopK选择
## 🚀 快速开始
### 编译和测试
#### 1. 全量编译和运行
```bash
# 编译并运行所有算法测试（默认行为）
./build_and_run.sh
# 仅编译所有算法，不运行测试
./build_and_run.sh --build-only
# 编译并运行单个算法测试
./build_and_run.sh --run_reduce # ReduceSum算法
./build_and_run.sh --run_sort # SortPair算法
./build_and_run.sh --run_topk # TopkPair算法
```
#### 2. 单独编译和运行
```bash
# 编译并运行ReduceSum算法（默认行为）
./build_and_run_reduce_sum.sh
# 仅编译ReduceSum算法，不运行测试
./build_and_run_reduce_sum.sh --build-only
# 编译并运行SortPair正确性测试
./build_and_run_sort_pair.sh --run correctness
# 编译并运行TopkPair性能测试
./build_and_run_topk_pair.sh --run performance
```
#### 3. 手动运行测试
```bash
./build/test_reducesum [correctness|performance|all]
./build/test_sortpair [correctness|performance|all]
./build/test_topkpair [correctness|performance|all]
```
## 📝 参赛指南
### 实现位置
参赛者需要在以下文件中替换Thrust实现：
- `src/reduce_sum_algorithm.maca` - 替换Thrust归约求和
- `src/sort_pair_algorithm.maca` - 替换Thrust稳定排序
- `src/topk_pair_algorithm.maca` - 替换Thrust TopK选择
- `reduce_sum_algorithm.maca` - 替换Thrust归约求和
- `sort_pair_algorithm.maca` - 替换Thrust稳定排序
- `topk_pair_algorithm.maca` - 替换Thrust TopK选择
### 算法要求
见competition_parallel_algorithms.md
@ -92,25 +45,21 @@
- 各数据规模的详细性能数据
- 升序/降序分别统计（适用时）
## 📁 项目结构
## 📁 提交内容结构
```
├── build_and_run.sh # 统一编译和运行脚本（默认编译+运行所有算法）
├── build_common.sh # 公共编译配置和函数
├── build_and_run_reduce_sum.sh # ReduceSum独立编译和运行脚本
├── build_and_run_sort_pair.sh # SortPair独立编译和运行脚本
├── build_and_run_topk_pair.sh # TopkPair独立编译和运行脚本
├── run.sh # 统一编译和运行脚本（默认编译+运行所有算法）
├── competition_parallel_algorithms.md # 详细题目说明
├── src/ # 算法实现和工具文件
│ ├── reduce_sum_algorithm.maca # 1. ReduceSum测试程序
│ ├── sort_pair_algorithm.maca # 2. SortPair测试程序
│ ├── topk_pair_algorithm.maca # 3. TopkPair测试程序
│── reduce_sum_algorithm.maca # 1. ReduceSum测试程序
│── sort_pair_algorithm.maca # 2. SortPair测试程序
│── topk_pair_algorithm.maca # 3. TopkPair测试程序
├── utils/ # 工具文件
│ ├── test_utils.h # 测试工具和CPU参考实现
│ ├── yaml_reporter.h # YAML性能报告生成器
│ └── performance_utils.h # 性能测试工具
├── final_results/reduce_sum_results.yaml #ReduceSum性能数据
├── final_results/sort_pair_results.yaml #替换Thrust稳定排序
└── final_results/topk_pair_results.yaml #TopkPair性能数据
├── reduce_sum_results.yaml #ReduceSum性能数据
├── sort_pair_results.yaml #替换Thrust稳定排序
└── topk_pair_results.yaml #TopkPair性能数据
```
## 🔧 开发工具
@ -134,7 +83,7 @@ mxcc -O3 -std=c++17 --extended-lambda -Isrc
|--------|--------|------|
| `COMPILER` | `mxcc` | CUDA编译器路径 |
| `COMPILER_FLAGS` | `-O3 -std=c++17 --extended-lambda` | 编译标志 |
| `INCLUDE_DIR` | `src` | 头文件目录 |
| `HEADER_DIR` | `utils` | 头文件目录 |
| `BUILD_DIR` | `build` | 构建输出目录 |
### 调试模式

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# 题目：
# 样例赛题说明
## GPU高性能并行计算算法优化
要求参赛者通过一个或多个global kernel 函数（允许配套 device 辅助函数），实现高性能算法。
在正确性、稳定性前提下，比拼算法性能。
# 1. ReduceSum算法优化
```cpp
template <typename InputT = float, typename OutputT = float>
@ -23,14 +23,12 @@ public:
* 系统将测试评估1M, 128M, 512M, 1G element number下的算法性能
* 假定输入d\_in数据量为num\_items
注意事项
* 累计误差不大于cpu double golden基准的0.5%
* 注意针对NAN和INF等异常值的处理
加分项
* 使用tensor core计算reduce
@ -62,14 +60,11 @@ public:
* 需要校验结果正确性
* 结果必须稳定排序
加分项
* 支持其他不同数据类型的排序，如half、double、int32_t等
* 覆盖更全面的数据范围，提供良好稳定的性能表现
# 3. Topk Pair算法优化
```cpp
template <typename KeyType, typename ValueType>
@ -95,7 +90,6 @@ public:
* 结果必须稳定排序
加分项
* 支持其他不同数据类型的键值对，实现类型通用算法

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@ -36,11 +36,11 @@ COMPILER=${COMPILER:-mxcc}
COMPILER_FLAGS=${COMPILER_FLAGS:-"-O3 -std=c++17 --extended-lambda -DRUN_FULL_TEST"}
# ***** 这里是关键修改点1：头文件目录 *****
# 现在头文件在 includes/ 目录下
# 现在头文件在 utils/ 目录下
HEADER_DIR=${HEADER_DIR:-utils}
# ***** 这里是关键修改点2：源文件目录 *****
# 现在源文件在 algorithms/ 目录下
# 现在源文件在 ./ 目录下
SOURCE_CODE_DIR=${SOURCE_CODE_DIR:-}
BUILD_DIR=${BUILD_DIR:-build}

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utils/test_utils.h → cp_template/utils/test_utils.h
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utils/yaml_reporter.h → cp_template/utils/yaml_reporter.h
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