Fast inference engine for Transformer models
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Updated
Sep 25, 2024 - C++
Fast inference engine for Transformer models
🎉 Modern CUDA Learn Notes with PyTorch: fp32, fp16, bf16, fp8/int8, flash_attn, sgemm, sgemv, warp/block reduce, dot, elementwise, softmax, layernorm, rmsnorm.
Tuned OpenCL BLAS
BLISlab: A Sandbox for Optimizing GEMM
The HPC toolbox: fused matrix multiplication, convolution, data-parallel strided tensor primitives, OpenMP facilities, SIMD, JIT Assembler, CPU detection, state-of-the-art vectorized BLAS for floats and integers
Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruction.
Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.
Stretching GPU performance for GEMMs and tensor contractions.
Fast multi-threaded matrix multiplication in C
DBCSR: Distributed Block Compressed Sparse Row matrix library
hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library
FP64 equivalent GEMM via Int8 Tensor Cores using the Ozaki scheme
Serial and parallel implementations of matrix multiplication
The simplest but fast implementation of matrix multiplication in CUDA.
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