making AI inference run really fast.
In this blog post, we walk through how to use Matrix Cores in HIP kernels, with a focus on low-precision data types such as FP16, FP8, and FP4, as well as the new family of Matrix Core instructions with exponent block scaling introduced in the AMD CDNA™4 architecture. Through code examples and illustrations, we provide the necessary knowledge to start programming Matrix Cores, covering modern…
This blog post focuses on a GPU implementation of SGEMM (Single-precision GEneral Matrix Multiply) operation defined as C := alpha*A*B + beta*C. We'll review the algorithm’s design and discuss optimization techniques such as inlined PTX, asynchronous memory copies, double-buffering, avoiding shared memory bank conflicts, and efficient coalesced storage through shared memory.
This blog post focuses on a GPU implementation of SGEMM (Single-precision GEneral Matrix Multiply) operation defined as C := alpha*A*B + beta*C. We'll review the algorithm’s design and discuss optimization techniques such as inlined PTX, asynchronous memory copies, double-buffering, avoiding shared memory bank conflicts, and efficient coalesced storage through shared memory.
A detailed blog post on optimizing multi-threaded matrix multiplication for x86 processors to achieve OpenBLAS/MKL-like performance. Tags: High-performance GEMM on CPU, Fast GEMM on CPU, High-performance matrix multiplication on CPU, Fast Matrix Multiplication on CPU, Matrix multiplication in C, GEMM in C, Matrix multiplication acceleration.
A detailed blog post on optimizing multi-threaded matrix multiplication for x86 processors to achieve OpenBLAS/MKL-like performance. Tags: High-performance GEMM on CPU, Fast GEMM on CPU, High-performance matrix multiplication on CPU, Fast Matrix Multiplication on CPU, Matrix multiplication in C, GEMM in C, Matrix multiplication acceleration.