找事做网站怎么弄,seo评测论坛,公司网站建设会议纪要,买域名和服务器做自己的网站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… 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 ArchitectureHow to calculate the Tensor Core FP16 performance of H100?NVIDIA H100 PCIe 80 GBNVIDIA H800 Tensor Core GPU
A100、A800、H100、H800差异 H100详细规格 H100 TensorCore FP16 理论算力计算公式
4096 FLOP/clk per SM.The H100 PCIE has 114 SMs114 x 4096 466944 FLOP/clkBoostClock:1620MHz114 x 4096 x1620M/1000/1000756 TFLOPS当前的卡最大频率为1980– 114 x 4096 x1980M/1000/1000924 TFLOPS
锁频
nvidia-smi -q -d SUPPORTED_CLOCKS
nvidia-smi -lgc 1980,1980
nvidia-smi --lock-memory-clocks-deferred2619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 pycudapytorch 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), devicedevice, dtypedtype)B torch.randn((K, N), devicedevice, dtypedtype) # 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
M2048
N2048
K40960
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(fNum:{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(790/92485%)
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: 798.54cublas 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), dtypecp.float16)# 创建 cuBLAS 句柄handle cublas.create() # 标量 alpha 和 betaalpha np.array(1, dtypenp.float16)beta np.array(0, dtypenp.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(fCUDA 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
M2048
N2048
K40960
matrix_size 1
for i in range(5):elapsed_time, gflops cublas_fp16_strided_batched_gemm(M,N,K,matrix_size,num_runs)print(fNum:{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(817/92488%)
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 --device0输出
Device 0: NVIDIA H800CUDA 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: ta0 tb0 m8192 n8192 k8192 alpha (0xbf800000, -1) beta (0x40000000, 2)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 0.04317784 sec GFLOPS25464.7sgemm test OK
Testing dgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0x0000000000000000, 0) beta (0x0000000000000000, 0)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 0.00023699 sec GFLOPS4.63952e06dgemm test OK Running N10 without streams Testing sgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0xbf800000, -1) beta (0x00000000, 0)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 0.22819090 sec GFLOPS48183.9sgemm test OK
Testing dgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0xbff0000000000000, -1) beta (0x0000000000000000, 0)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 11.56301594 sec GFLOPS950.887dgemm test OK Running N10 with streams Testing sgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0x40000000, 2) beta (0x40000000, 2)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 0.23047590 sec GFLOPS47706.1sgemm test OK
Testing dgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0xbff0000000000000, -1) beta (0x0000000000000000, 0)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 11.38687706 sec GFLOPS965.595dgemm test OK Running N10 batched Testing sgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0x3f800000, 1) beta (0xbf800000, -1)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 0.21581888 sec GFLOPS50946sgemm test OK
Testing dgemm
#### args: ta0 tb0 m8192 n8192 k8192 alpha (0xbff0000000000000, -1) beta (0x4000000000000000, 2)
#### args: lda8192 ldb8192 ldc8192
^^^^ elapsed 11.38980007 sec GFLOPS965.348dgemm test OKTest Summary
0 error(s)
