high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

How much can we gain from Tensor Kernel Fusion on GPUs?

Wei Sun, Ang Li, Sander Stuijk, Henk Corporaal

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia A100, nVidia H100

June 16, 2024 by hgpu

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Afzal Ahmad, Linfeng Du, Wei Zhang

View

Download (PDF)

Source codes

Tags: BLAS, Computer science, FPGA, GEMM, Linear Algebra, Machine learning, Matrix multiplication, OpenCL, Package

June 9, 2024 by hgpu

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

View

Download (PDF)

Source codes

Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer, Diana Marculescu, Aman Arora

View

Download (PDF)

Tags: AI, Computer science, Deep learning, FPGA, GEMM, Matrix multiplication

April 21, 2024 by hgpu

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Xinyi Li, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, Ganesh Gopalakrishnan

View

Download (PDF)

Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, Hardware Architecture, HPC, Matrix multiplication, nVidia, nVidia A100, nVidia H100, nVidia V100, PTX

March 10, 2024 by hgpu

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim, Jiwoong Choi, Minkyu Kim, Hyungjun Kim

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Mixed precision, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia RTX A6000, Package

February 18, 2024 by hgpu

Minuet: Accelerating 3D Sparse Convolutions on GPUs

Jiacheng Yang, Christina Giannoula, Jun Wu, Mostafa Elhoushi, James Gleeson, Gennady Pekhimenko

View

Download (PDF)

Source codes

Tags: Cloud, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 2070, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, Package, Performance, PyTorch, Tesla A100

January 21, 2024 by hgpu

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Benjamin Brock, Aydın Buluç, Katherine Yelick

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, nVidia DGX-2, Sparse matrix, Tesla V100

December 3, 2023 by hgpu

Evaluation of FPGA-based high performance computing platforms

Martin Frick-Lundgren

View

Download (PDF)

Source codes

Tags: Computer science, FPGA, HPC, Intel, Intel UHD 630, Matrix multiplication, OpenCL, Package, SYCL, Thesis

November 27, 2023 by hgpu

Solving MaxSAT with Matrix Multiplication

David Warde-Farley, Vinod Nair, Yujia Li, Ivan Lobov, Felix Gimeno, Simon Osindero

View

Download (PDF)

Tags: AI, Artificial intelligence, Computer science, Matrix multiplication, Neural networks, TPU

November 12, 2023 by hgpu

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Mohammad Zubair, Christoph Bauinger

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Intel, Intel Data Center GPU Max 1550, Linear Algebra, Machine learning, Mathematical Software, Matrix multiplication, nVidia, nVidia V100, Sparse matrix, SYCL

November 5, 2023 by hgpu

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Pablo Antonio Martínez Sánchez

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, performance portability, Thesis

July 16, 2023 by hgpu

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

How much can we gain from Tensor Kernel Fusion on GPUs?

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Evaluation of FPGA-based high performance computing platforms

Solving MaxSAT with Matrix Multiplication

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Most viewed papers (last 30 days)