high performance computing on graphics processing units: hgpu.org

Federico Simmross-Wattenberg, Manuel Rodriguez-Cayetano, Javier Royuela-del-Val, Elena Martin-Gonzalez, Elisa Moya-Saez, Marcos Martin-Fernandez, Carlos Alberola-Lopez

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Tags: Heterogeneous systems, Image processing, Medicine, OpenCL, Package

August 5, 2018 by hgpu

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Rohan Garg, Apoore Mohan, Michael Sullivan, Gene Cooperman

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla P100

August 5, 2018 by hgpu

A Survey on Agent-based Simulation using Hardware Accelerators

Jiajian Xiao, Philipp Andelfinger, David Eckhoff, Wentong Cai, Alois Knoll

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Overview

July 5, 2018 by hgpu

Reducing the Cost of Heuristic Generation with Machine Learning

William F. Ogilvie

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Thesis

July 1, 2018 by hgpu

Introducing Parallelism to the Ranges TS

Gordon Brown, Christopher Di Bella, Michael Haidl, Toomas Remmelg, Ruyman Reyes, Michel Steuwer

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Tags: Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 28, 2018 by hgpu

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Tim Cramer

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Tags: Computer science, Heterogeneous systems, HPC, Intel Xeon Phi, Memory model, OpenMP, Parallel programming, Thesis

June 28, 2018 by hgpu

Improving tasks throughput on accelerators using OpenCL command concurrency

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

June 28, 2018 by hgpu

Research on the simulation of PF-LBM model based on MPI+CUDA mixed granularity parallel

Changsheng Zhu, Jieqiong Liu, Li Feng, Xin Deng

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Tesla M2090

June 24, 2018 by hgpu

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

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

HSTREAM: A directive-based language extension for heterogeneous stream computing

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

A Survey on Agent-based Simulation using Hardware Accelerators

Reducing the Cost of Heuristic Generation with Machine Learning

Introducing Parallelism to the Ranges TS

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Improving tasks throughput on accelerators using OpenCL command concurrency

Research on the simulation of PF-LBM model based on MPI+CUDA mixed granularity parallel

Recent source codes

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

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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