libxsmm-2.1.0-4.fc45.src.rpm

LIBXSMM is high performance library for small dense and sparse linear algebra opertions incl. GEMM and elementwise primities often seen in deep learning applications. It also serves as reference implementation of Tensor Processing Primitives (TPP), a programming abstraction for efficient and portable deep learning and HPC workloads. With version 2.0, LIBXSMM focuses on providing a complete and architecture-portable set of TPPs (small dense and sparse matrix operations as well as element-wise, GEMM, and BRGEMM primitives) from which higher-level operators such as convolutions, fully-connected layers, normalization, and pooling are composed. LIBXSMM targets Intel Architecture with Intel SSE, Intel AVX, Intel AVX2, Intel AVX-512 (with VNNI and Bfloat16), and Intel AMX (Advanced Matrix Extensions), AArch64 (NEON, SVE, and SME), RISC-V (RVV), and PowerPC 64-bit little-endian (POWER10 with VSX and MMA). Code generation is mainly based on Just-In-Time (JIT) code specialization for compiler-independent performance (matrix multiplications, matrix transpose/copy, sparse functionality, and tensor primitives). LIBXSMM is suitable for "build once and deploy everywhere", i.e., no special target flags are needed to exploit the available performance. Supported GEMM datatypes are: FP64, FP32, FP16, bfloat16, BF8, HF8, MXBF8, MXHF8, int16, int8, MXBF6, MXHF6, MXFP4, int4, int2 and int1. Additionally, various non-standard low precision combinations are supported.