首先,包括cuSPARSELt标头,设置一些设备指针和数据结构,并初始化cuSPARSELt句柄。 #include <cusparseLt.h> // cusparseLt header // Device pointers and coefficient definitions float alpha = 1.0f; float beta = 0.0f; __half* dA = ... __half* dB = ... __half* dC = ... // cusparseLt data...
首先,包括cuSPARSELt标头,设置一些设备指针和数据结构,并初始化cuSPARSELt句柄。 #include <cusparseLt.h> // cusparseLt header // Device pointers and coefficient definitions float alpha = 1.0f; float beta = 0.0f; __half* dA = ... __half* dB = ... __half* dC = ... // cusparseLt data...
首先,包括cuSPARSELt标头,设置一些设备指针和数据结构,并初始化cuSPARSELt句柄。 #include <cusparseLt.h> // cusparseLt header // Device pointers and coefficient definitions float alpha = 1.0f; float beta = 0.0f; __half* dA = ... __half* dB = ... __half* dC = ... // cusparseLt data...
cuSPARSELt v0.6.3# Resolved issues Sparse GEMM could produce incorrect results on Arm64 if cusparseLtSpMMACompressSize2() and cusparseLtSpMMACompress() are used. Compatibility notes: Add support for Ubuntu 24.04. cuSPARSELt v0.6.2# New Features: Introduced Orin support (SM 8.7). Improved perfo...
NVIDIA cuSPARSELt v0.2.0提高激活函数 英伟达推出 cuSPARSELt,版本0 .2.0 ,它提高了激活函数、偏差向量和批处理稀疏 GEMM 的性能。 NVIDIA CUSPASSELT 是一个高性能 CUDA 库,专用于一般矩阵运算,其中至少有一个操作数是稀疏矩阵: 在这个等式中, OP(A) 和 OP(B) 指的是原位操作,例如转置和非转置。
Examples:cuSPARSELt Example 1,cuSPARSELt Example 2 Blog post: Key Features# NVIDIA Sparse MMA tensor coresupport Mixed-precision computation support: Input A/B Input C Output D Compute Support arch FP32 FP32 FP32 FP32 SM8.0,8.6,8.7,9.0 ...
🐛 Describe the bug When calling into cuSparseLt function from different threads, we can get this error. Notably this happens in the BW pass. I have a small repro code - if the BW pass runs on the main thread, the error does not show up. ...
python gpu numpy cuda cublas scipy tensor cudnn rocm cupy cusolver nccl curand cusparse nvrtc cutensor nvtx cusparselt Updated Nov 6, 2024 Python Improve this page Add a description, image, and links to the cusparselt topic page so that developers can more easily learn about it. Curate...
cuSPARSELt is currently available for Windows and Linux for x86-64 and Linux for arm64, requires CUDA 11.x or newer. Select Operating System and Package Type Click on the green buttons that describe your target operating system. Only supported operating system and platforms will be shown. By ...
The cuSPARSELt library makes it easy to exploit NVIDIA Sparse Tensor Core operations, significantly improving the performance of matrix-matrix multiplication for deep learning applications without reducing network’s accuracy. The library also provides utilities for matrix compression, pruning, and performanc...