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GPU Accelerated Strong and Branching Bisimilarity Checking

Anton Wijs

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Tags: Algorithms, Computer science, CUDA, nVidia, Package, Tesla K20

April 8, 2015 by hgpu

The Design and Implementation of a Verification Technique for GPU Kernels

Adam Betts, Nathan Chong, Alastair F. Donaldson, Jeroen Ketema, Shaz Qadeer, Paul Thomson, John Wickerson

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Tags: ARM, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenCL, Package, Tesla C2050

April 4, 2015 by hgpu

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Gloria Ortega Lopez

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Tags: Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C2050, Tesla M2070, Tesla M2090, Thesis, Tomography

March 30, 2015 by hgpu

Loo.py: From Fortran to performance via transformation and substitution rules

Andreas Klockner

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Tags: Code generation, Computer science, Fortran, OpenCL, Package, Python

March 28, 2015 by hgpu

Pseudorandom Numbers Generation for Monte Carlo Simulations on GPUs: OpenCL Approach

Vadim Demchik

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 560 M, nVidia GeForce GTX 780, OpenCL, Package, Pseudo-random number generators, Review, Tesla K40

March 25, 2015 by hgpu

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

Ken Miura, Tatsuya Harada

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Tags: Algorithms, Computer science, Java, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, WebCL

March 20, 2015 by hgpu

Fast Sparse Matrix Multiplication on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Sorting, Sparse matrix, Tesla K40

March 18, 2015 by hgpu

Counting Triangles in Large Graphs on GPU

Adam Polak

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Tags: Clustering, Computer science, CUDA, Graph theory, MapReduce, nVidia, Package, Tesla C2050

March 3, 2015 by hgpu

MILJS: Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada

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Tags: Computer science, Java, Machine learning, Mathematical Software, OpenCL, Package, Python

March 2, 2015 by hgpu

Exploring Design Space of 3D NVM and eDRAM Caches Using DESTINY Tool (open-source code)

Sparsh Mittal, Matt Poremba, Jeffrey Vetter, Yuan Xie

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Tags: Architecture, area, cache, cpu, energy, GPGPU, latency modeling, Memory, Package

February 22, 2015 by sparsh0mittal

Reproducible Triangular Solvers for High-Performance Computing

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, Computer science, Linear Algebra, nVidia, nVidia Quadro K5000, OpenCL, Package, Tesla K20

February 19, 2015 by hgpu

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

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high performance computing on graphics processing units: hgpu.org

Packages

GPU Accelerated Strong and Branching Bisimilarity Checking

The Design and Implementation of a Verification Technique for GPU Kernels

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Loo.py: From Fortran to performance via transformation and substitution rules

Pseudorandom Numbers Generation for Monte Carlo Simulations on GPUs: OpenCL Approach

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

Fast Sparse Matrix Multiplication on GPU

Counting Triangles in Large Graphs on GPU

MILJS: Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

Exploring Design Space of 3D NVM and eDRAM Caches Using DESTINY Tool (open-source code)

Reproducible Triangular Solvers for High-Performance Computing

Recent source codes

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

HDM: Home made Diffusion Models

General Matrix Multiplication (GEMM)

CrossTL: Universal Programming Language & Translator

TBD-GPU

DG-SWEM - The Discontinuous Galerkin Shallow Water Equation Model

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

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