H800基础能力测试

H800基础能力测试

码农世界 2024-05-24 前端 114 次浏览 0个评论

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

      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 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 time
def 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 FLOPS
    start = 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 GFLOPS
    num_operations = 2 * M*N*K
    gflops = 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 FLOPs
    total_flops = 2 * M*K*N    
    # 计算 GFLOPS
    gflops = 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 cuda
def 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 beta
    alpha = 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 GFLOPS
    num_operations = 2 * M*N*K*batch_size
    gflops = num_operations / (elapsed_time * 1e-3) / 1e12    
    return elapsed_time, gflops    
    elapsed_time = (end_time - start_time) / num_runs
    num_ops = 2*M*K*N*batch_size
    gflops = 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 / 12.2
  CUDA Capability Major/Minor version number:    9.0
  Total amount of global memory:                 81008 MBytes (84942979072 bytes)
  (132) Multiprocessors, (128) CUDA Cores/MP:    16896 CUDA Cores
  GPU Max Clock rate:                            1980 MHz (1.98 GHz)
  Memory Clock rate:                             2619 Mhz
  Memory Bus Width:                              5120-bit
  L2 Cache Size:                                 52428800 bytes
  Maximum 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 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total shared memory per multiprocessor:        233472 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max 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 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 3 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 215 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
-----------------------------------------------------------------------------------------------------
[CUDA Bandwidth Test] - Starting...
Running on...
 Device 0: NVIDIA H800
 Quick Mode
 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     55.2
 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     55.3
 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(GB/s)
   32000000                     2085.3
Result = 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 OK
Test Summary
0 error(s)

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