code large_stringlengths 193 899 | workload_type large_stringclasses 15
values | gpu_name large_stringclasses 12
values | gpu_features large_stringclasses 12
values | runtime_ms float64 0.01 4.35k | flops float64 30 21,045B | memory_bytes int64 160 13.2B | arithmetic_intensity float64 0 6.37k | dtype_bytes int64 2 4 | workload_params large_stringlengths 11 79 |
|---|---|---|---|---|---|---|---|---|---|
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 0.231801 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.146721 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.215818 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.088805 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.078231 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.126939 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.102628 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.208456 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.104674 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.030416 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.053803 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 512) x (512, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 512, dtype=torch.float16, device='cuda')
B = torch.randn(512, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchron... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.218571 | 8,589,934,592 | 23,068,672 | 372.363636 | 2 | {"M": 2048, "N": 4096, "K": 512, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 2.705993 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 1.337467 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.983054 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 1.052332 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 1.193626 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.762561 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.273776 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.549191 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.268119 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.284581 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.547993 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float32, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.713003 | 17,179,869,184 | 58,720,256 | 292.571429 | 4 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 0.352242 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.218201 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.419711 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.111362 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.112177 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.220898 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.173964 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.343261 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.215936 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.072875 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.085116 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 1024) x (1024, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 1024, dtype=torch.float16, device='cuda')
B = torch.randn(1024, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.271669 | 17,179,869,184 | 29,360,128 | 585.142857 | 2 | {"M": 2048, "N": 4096, "K": 1024, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 5.119941 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 3.28646 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.812782 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 1.78633 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 2.62615 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 1.6538 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.714302 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.490865 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.672814 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.675306 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.76656 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float32, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.467819 | 34,359,738,368 | 83,886,080 | 409.6 | 4 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 0.68708 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.384647 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.839131 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.232214 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.218204 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.381694 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.278899 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.762194 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.30858 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.059136 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.122191 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 2048) x (2048, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 2048, dtype=torch.float16, device='cuda')
B = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.618273 | 34,359,738,368 | 41,943,040 | 819.2 | 2 | {"M": 2048, "N": 4096, "K": 2048, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 13.201886 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 5.764345 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 3.130692 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 4.746396 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 3.619875 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 2.774833 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.838077 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.881737 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.992339 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 1.614796 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 1.936242 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float32, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 2.625993 | 68,719,476,736 | 134,217,728 | 512 | 4 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 1.844604 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.774116 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.750791 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.275539 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.29606 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.634828 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.413058 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.477992 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.752431 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.112048 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.155673 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (2048, 4096) x (4096, 4096) -> (2048, 4096)
C = torch.matmul(A, B)
return C
A = torch.randn(2048, 4096, dtype=torch.float16, device='cuda')
B = torch.randn(4096, 4096, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.sync... | matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 1.318258 | 68,719,476,736 | 67,108,864 | 1,024 | 2 | {"M": 2048, "N": 4096, "K": 4096, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 0.055831 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.053544 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.024269 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.027134 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA A100 80GB | {"gpu_name": "NVIDIA A100 80GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.043588 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA L4 | {"gpu_name": "NVIDIA L4", "cuda_cores": 7424, "tensor_cores": 232, "memory_gb": 24, "memory_bandwidth_gbps": 300, "base_clock_mhz": 795, "boost_clock_mhz": 2040, "sm_count": 58, "fp32_tflops": 30.3, "fp16_tflops": 121, "tdp_watts": 72, "compute_capability": 8.9, "l2_cache_mb": 48} | 0.039698 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA L40S | {"gpu_name": "NVIDIA L40S", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbps": 864, "base_clock_mhz": 1110, "boost_clock_mhz": 2520, "sm_count": 142, "fp32_tflops": 91.6, "fp16_tflops": 183.2, "tdp_watts": 350, "compute_capability": 8.9, "l2_cache_mb": 96} | 0.052292 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA RTX 3090 | {"gpu_name": "NVIDIA RTX 3090", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbps": 936, "base_clock_mhz": 1395, "boost_clock_mhz": 1695, "sm_count": 82, "fp32_tflops": 35.6, "fp16_tflops": 71, "tdp_watts": 350, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.050726 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA RTX 4090 | {"gpu_name": "NVIDIA RTX 4090", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbps": 1008, "base_clock_mhz": 2235, "boost_clock_mhz": 2520, "sm_count": 128, "fp32_tflops": 82.6, "fp16_tflops": 165.2, "tdp_watts": 450, "compute_capability": 8.9, "l2_cache_mb": 72} | 0.031233 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA H100 SXM | {"gpu_name": "NVIDIA H100 SXM", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbps": 3350, "base_clock_mhz": 1095, "boost_clock_mhz": 1830, "sm_count": 132, "fp32_tflops": 67, "fp16_tflops": 989, "tdp_watts": 700, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.05762 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA H100 PCIe | {"gpu_name": "NVIDIA H100 PCIe", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 80, "memory_bandwidth_gbps": 2039, "base_clock_mhz": 1095, "boost_clock_mhz": 1620, "sm_count": 114, "fp32_tflops": 48, "fp16_tflops": 756, "tdp_watts": 350, "compute_capability": 9.0, "l2_cache_mb": 50} | 0.043341 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float32, device='cuda')
B = torch.randn(64, 64, dtype=torch.float32, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA RTX A6000 | {"gpu_name": "NVIDIA RTX A6000", "cuda_cores": 10752, "tensor_cores": 336, "memory_gb": 48, "memory_bandwidth_gbps": 768, "base_clock_mhz": 1410, "boost_clock_mhz": 1860, "sm_count": 84, "fp32_tflops": 38.7, "fp16_tflops": 77.4, "tdp_watts": 300, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.038892 | 33,554,432 | 2,113,536 | 15.875969 | 4 | {"M": 4096, "N": 64, "K": 64, "dtype": "float32"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float16, device='cuda')
B = torch.randn(64, 64, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA T4 | {"gpu_name": "NVIDIA T4", "cuda_cores": 2560, "tensor_cores": 320, "memory_gb": 16, "memory_bandwidth_gbps": 320, "base_clock_mhz": 585, "boost_clock_mhz": 1590, "sm_count": 40, "fp32_tflops": 8.1, "fp16_tflops": 65, "tdp_watts": 70, "compute_capability": 7.5, "l2_cache_mb": 4} | 0.056035 | 33,554,432 | 1,056,768 | 31.751938 | 2 | {"M": 4096, "N": 64, "K": 64, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float16, device='cuda')
B = torch.randn(64, 64, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA V100 | {"gpu_name": "NVIDIA V100", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbps": 900, "base_clock_mhz": 1230, "boost_clock_mhz": 1530, "sm_count": 80, "fp32_tflops": 15.7, "fp16_tflops": 125, "tdp_watts": 300, "compute_capability": 7.0, "l2_cache_mb": 6} | 0.042206 | 33,554,432 | 1,056,768 | 31.751938 | 2 | {"M": 4096, "N": 64, "K": 64, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float16, device='cuda')
B = torch.randn(64, 64, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA A10G | {"gpu_name": "NVIDIA A10G", "cuda_cores": 9216, "tensor_cores": 288, "memory_gb": 24, "memory_bandwidth_gbps": 600, "base_clock_mhz": 885, "boost_clock_mhz": 1695, "sm_count": 80, "fp32_tflops": 31.2, "fp16_tflops": 62.5, "tdp_watts": 150, "compute_capability": 8.6, "l2_cache_mb": 6} | 0.063411 | 33,554,432 | 1,056,768 | 31.751938 | 2 | {"M": 4096, "N": 64, "K": 64, "dtype": "float16"} |
import torch
def matmul_kernel(A, B):
# Matrix multiplication: (4096, 64) x (64, 64) -> (4096, 64)
C = torch.matmul(A, B)
return C
A = torch.randn(4096, 64, dtype=torch.float16, device='cuda')
B = torch.randn(64, 64, dtype=torch.float16, device='cuda')
C = matmul_kernel(A, B)
torch.cuda.synchronize()
| matmul | NVIDIA A100 40GB | {"gpu_name": "NVIDIA A100 40GB", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbps": 1555, "base_clock_mhz": 765, "boost_clock_mhz": 1410, "sm_count": 108, "fp32_tflops": 19.5, "fp16_tflops": 312, "tdp_watts": 400, "compute_capability": 8.0, "l2_cache_mb": 40} | 0.023066 | 33,554,432 | 1,056,768 | 31.751938 | 2 | {"M": 4096, "N": 64, "K": 64, "dtype": "float16"} |
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