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ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

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Tags: Algorithms, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, Tesla K40, Tesla M40, Tesla P100

February 16, 2020 by hgpu

A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices

Chetan Jhurani, Paul Mullowney

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Tags: BLAS, CUBLAS, CUDA, Dense linear algebra, GEMM, Linear Algebra, nVidia, Parallel programming, Tesla K20

April 9, 2013 by chetan.jhurani

HPCToolkit

Measurement and Analysis of GPU-accelerated Applications with HPCToolkit

NVPTX-SPIR-V Translator

Supporting CUDA for an extended RISC-V GPU architecture

Taichi Lattice Boltmann

Data-Oriented Language Implementation of Lattice-Boltzmann Method for Dense and Sparse Geometries

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

ScaleHLS Project

ScaleHLS: Scalable High-Level Synthesis through MLIR

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