H800基础能力测试

本文主要是介绍H800基础能力测试，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

H800基础能力测试

参考链接
A100、A800、H100、H800差异
H100详细规格
H100 TensorCore FP16 理论算力计算公式
锁频
安装依赖
pytorch FP16算力测试
cublas FP16算力测试
运行cuda-samples

本文记录了H800基础测试步骤及测试结果

参考链接

NVIDIA H100 Tensor Core GPU Architecture
How to calculate the Tensor Core FP16 performance of H100?
NVIDIA H100 PCIe 80 GB
NVIDIA H800 Tensor Core GPU

A100、A800、H100、H800差异

在这里插入图片描述

H100详细规格

在这里插入图片描述

H100 TensorCore FP16 理论算力计算公式

4096 FLOP/clk per SM.
The H100 PCIE has 114 SMs
114 x 4096 = 466944 FLOP/clk
BoostClock:1620MHz
114 x 4096 x1620M/1000/1000=756 TFLOPS
当前的卡最大频率为1980–> 114 x 4096 x1980M/1000/1000=924 TFLOPS

锁频

nvidia-smi -q -d SUPPORTED_CLOCKS
nvidia-smi -lgc 1980,1980 
nvidia-smi --lock-memory-clocks-deferred=2619

安装依赖

pip3 install https://github.com/cupy/cupy/releases/download/v13.1.0/cupy_cuda12x-13.1.0-cp310-cp310-manylinux2014_x86_64.whl
pip3 install pycuda

pytorch FP16算力测试

tee torch_flops.py <<-'EOF'
import pycuda.autoinit
import pycuda.driver as cuda
import torch
import timedef benchmark_pytorch_fp16(M,N,K, num_runs):# 确保使用 GPU 并设置数据类型为半精度浮点数 (float16)device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')dtype = torch.float16# 生成随机矩阵A = torch.randn((M, K), device=device, dtype=dtype)B = torch.randn((K, N), device=device, dtype=dtype)    # 预热 GPU，进行一次矩阵乘法C = torch.matmul(A, B)    # 记录开始时间start_time = time.time()    # 多次进行矩阵乘法，计算 FLOPSstart = cuda.Event()end = cuda.Event()start.record()    for _ in range(num_runs):C = torch.mm(A, B)    end.record()torch.cuda.synchronize()    elapsed_time = start.time_till(end) / num_runs    # 计算 GFLOPSnum_operations = 2 * M*N*Kgflops = num_operations / (elapsed_time * 1e-3) / 1e12    return elapsed_time, gflops# 记录结束时间end_time = time.time()    # 计算平均运行时间elapsed_time = (end_time - start_time) / num_runs    # 计算总的 FLOPstotal_flops = 2 * M*K*N    # 计算 GFLOPSgflops = total_flops / elapsed_time / 1e12    return elapsed_time, gflops
# 设置矩阵大小和运行次数
num_runs = 32
M=2048
N=2048
K=40960
for i in range(5):# 运行基准测试elapsed_time, gflops = benchmark_pytorch_fp16(M,N,K, num_runs)# 输出结果print(f"Num:{i} 矩阵乘法大小: {M}x{K}X{N} 平均运行时间: {elapsed_time:.6f} 秒 TFLOPS: {gflops:.2f}")time.sleep(0.1)
EOF
python3 torch_flops.py

输出(790/924=85%)

Num:0 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.441580 秒 TFLOPS: 778.11
Num:1 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.430380 秒 TFLOPS: 798.36
Num:2 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.430523 秒 TFLOPS: 798.09
Num:3 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.430742 秒 TFLOPS: 797.69
Num:4 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.430283 秒 TFLOPS: 798.54

cublas FP16算力测试

tee cublas_flops.py <<-'EOF'
import cupy as cp
import numpy as np
from cupy._core import _dtype
from cupy.cuda import cublas
from time import time
from ctypes import c_void_p, c_float, cast, pointer, byref
import pycuda.autoinit
import pycuda.driver as cudadef cublas_fp16_strided_batched_gemm(M,N,K, batch_size, num_runs):# 创建随机半精度矩阵并转换为 CuPy 数组cp.cuda.Device(0).use()A = cp.random.randn(batch_size, M, K).astype(cp.float16)B = cp.random.randn(batch_size, K, N).astype(cp.float16)C = cp.empty((batch_size, M, N), dtype=cp.float16)# 创建 cuBLAS 句柄handle = cublas.create()    # 标量 alpha 和 betaalpha = np.array(1, dtype=np.float16)beta = np.array(0, dtype=np.float16)    cublas.setMathMode(handle, cublas.CUBLAS_TENSOR_OP_MATH)algo = cublas.CUBLAS_GEMM_DEFAULT_TENSOR_OP    try:# Warm-up (预热)for j in range(1):cublas.gemmStridedBatchedEx(handle,cublas.CUBLAS_OP_N, cublas.CUBLAS_OP_N,M, N, K,alpha.ctypes.data, A.data.ptr,_dtype.to_cuda_dtype(A.dtype,True), M, M * K,B.data.ptr, _dtype.to_cuda_dtype(B.dtype,True), K, K * N,beta.ctypes.data, C.data.ptr, _dtype.to_cuda_dtype(C.dtype,True), M, M * N,batch_size,_dtype.to_cuda_dtype(C.dtype,True), algo)cp.cuda.Device(0).synchronize()    # 实际基准测试start = cuda.Event()end = cuda.Event()start.record()start_time = time()for _ in range(num_runs):cublas.gemmStridedBatchedEx(handle,cublas.CUBLAS_OP_N, cublas.CUBLAS_OP_N,M, N, K,alpha.ctypes.data, A.data.ptr,_dtype.to_cuda_dtype(A.dtype,True), M, M * K,B.data.ptr, _dtype.to_cuda_dtype(B.dtype,True), K, K * N,beta.ctypes.data, C.data.ptr, _dtype.to_cuda_dtype(C.dtype,True), M, M * N,batch_size,_dtype.to_cuda_dtype(C.dtype,True), algo)end.record()cp.cuda.Device(0).synchronize()end_time = time()    except cp.cuda.runtime.CUDARuntimeError as e:print(f"CUDA 运行时错误: {e}")cublas.destroy(handle)return None, None    elapsed_time = start.time_till(end) / num_runs    # 计算 GFLOPSnum_operations = 2 * M*N*K*batch_sizegflops = num_operations / (elapsed_time * 1e-3) / 1e12    return elapsed_time, gflops    elapsed_time = (end_time - start_time) / num_runsnum_ops = 2*M*K*N*batch_sizegflops = num_ops / elapsed_time / 1e12    cublas.destroy(handle)    return elapsed_time, gflops
num_runs = 32
M=2048
N=2048
K=40960
matrix_size = 1
for i in range(5):elapsed_time, gflops = cublas_fp16_strided_batched_gemm(M,N,K,matrix_size,num_runs)print(f"Num:{i} 矩阵乘法大小: {M}x{K}X{N} 平均运行时间: {elapsed_time:.6f} 秒 TFLOPS: {gflops:.2f}")
EOF
python3 cublas_flops.py

输出(817/924=88%)

Num:0 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.421070 秒 TFLOPS: 816.01
Num:1 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.420407 秒 TFLOPS: 817.30
Num:2 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.420305 秒 TFLOPS: 817.50
Num:3 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.420304 秒 TFLOPS: 817.50
Num:4 矩阵乘法大小: 2048x40960X2048 平均运行时间: 0.420554 秒 TFLOPS: 817.01

运行cuda-samples

git clone https://www.github.com/nvidia/cuda-samples
cd cuda-samples/Samples/1_Utilities/deviceQuery
make clean && make
./deviceQuery
cd ../bandwidthTest/
make clean && make
./bandwidthTest
cd ../../4_CUDA_Libraries/batchCUBLAS/
make clean && make
./batchCUBLAS -m8192 -n8192 -k8192 --device=0

输出

Device 0: "NVIDIA H800"CUDA Driver Version / Runtime Version          12.2 / 12.2CUDA Capability Major/Minor version number:    9.0Total amount of global memory:                 81008 MBytes (84942979072 bytes)(132) Multiprocessors, (128) CUDA Cores/MP:    16896 CUDA CoresGPU Max Clock rate:                            1980 MHz (1.98 GHz)Memory Clock rate:                             2619 MhzMemory Bus Width:                              5120-bitL2 Cache Size:                                 52428800 bytesMaximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layersMaximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layersTotal amount of constant memory:               65536 bytesTotal amount of shared memory per block:       49152 bytesTotal shared memory per multiprocessor:        233472 bytesTotal number of registers available per block: 65536Warp size:                                     32Maximum number of threads per multiprocessor:  2048Maximum number of threads per block:           1024Max dimension size of a thread block (x,y,z): (1024, 1024, 64)Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)Maximum memory pitch:                          2147483647 bytesTexture alignment:                             512 bytesConcurrent copy and kernel execution:          Yes with 3 copy engine(s)Run time limit on kernels:                     NoIntegrated GPU sharing Host Memory:            NoSupport host page-locked memory mapping:       YesAlignment requirement for Surfaces:            YesDevice has ECC support:                        EnabledDevice supports Unified Addressing (UVA):      YesDevice supports Managed Memory:                YesDevice supports Compute Preemption:            YesSupports Cooperative Kernel Launch:            YesSupports MultiDevice Co-op Kernel Launch:      YesDevice PCI Domain ID / Bus ID / location ID:   0 / 215 / 0Compute Mode:< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
-----------------------------------------------------------------------------------------------------[CUDA Bandwidth Test] - Starting...
Running on...Device 0: NVIDIA H800Quick ModeHost to Device Bandwidth, 1 Device(s)PINNED Memory TransfersTransfer Size (Bytes)        Bandwidth(GB/s)32000000                     55.2Device to Host Bandwidth, 1 Device(s)PINNED Memory TransfersTransfer Size (Bytes)        Bandwidth(GB/s)32000000                     55.3Device to Device Bandwidth, 1 Device(s)PINNED Memory TransfersTransfer Size (Bytes)        Bandwidth(GB/s)32000000                     2085.3Result = PASS-----------------------------------------------------------------------------------------------------==== Running single kernels ====Testing sgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0xbf800000, -1) beta= (0x40000000, 2)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 0.04317784 sec  GFLOPS=25464.7
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0x0000000000000000, 0) beta= (0x0000000000000000, 0)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 0.00023699 sec  GFLOPS=4.63952e+06
@@@@ dgemm test OK==== Running N=10 without streams ====Testing sgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0xbf800000, -1) beta= (0x00000000, 0)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 0.22819090 sec  GFLOPS=48183.9
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 11.56301594 sec  GFLOPS=950.887
@@@@ dgemm test OK==== Running N=10 with streams ====Testing sgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0x40000000, 2) beta= (0x40000000, 2)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 0.23047590 sec  GFLOPS=47706.1
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 11.38687706 sec  GFLOPS=965.595
@@@@ dgemm test OK==== Running N=10 batched ====Testing sgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0x3f800000, 1) beta= (0xbf800000, -1)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 0.21581888 sec  GFLOPS=50946
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=8192 n=8192 k=8192  alpha = (0xbff0000000000000, -1) beta= (0x4000000000000000, 2)
#### args: lda=8192 ldb=8192 ldc=8192
^^^^ elapsed = 11.38980007 sec  GFLOPS=965.348
@@@@ dgemm test OKTest Summary
0 error(s)