high performance computing on graphics processing units: hgpu.org

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On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

A readahead prefetcher for GPU file system layer

Vasilis Dimitsas, Mark Silberstein

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Tags: Benchmarking, Computer science, CUDA, nVidia, Prefetch, Tesla K40

September 19, 2021 by hgpu

Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA

Javier Prades Gasulla

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Tags: Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Tesla K40, Tesla P100, Thesis, Virtualization

June 27, 2021 by hgpu

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Xuewen Cui

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Tags: Artificial intelligence, Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, OpenMP, Performance, Tesla K40, Tesla P100, Tesla V100, Thesis

December 20, 2020 by hgpu

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Min Li, Yulong Ao, Chao Yang

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Tags: Computer science, CUDA, Machine learning, nVidia, Sparse matrix, Tesla K40, Tesla P100, Tesla V100

July 5, 2020 by hgpu

GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices

Utpal Kiran, Sachin Singh Gautam, Deepak Sharma

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Sparse matrix, Tesla K40

June 28, 2020 by hgpu

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee, Aparna Chandramowlishwaran

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, TensorFlow, Tesla K40

May 31, 2020 by hgpu

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Tongsheng Geng, Marcos Amaris, Stephane Zuckerman, Alfredo Goldman, Guang R. Gao, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, Performance, Sparse matrix, Task scheduling, Tesla K20, Tesla K40

May 24, 2020 by hgpu

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

Hardware/Software Co-Design for Data-Intensive Genomics Workloads

Nicola Cadenelli

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Tags: Algorithms, Computer science, FPGA, nVidia, OpenCL, performance portability, Tesla K40, Thesis

January 26, 2020 by hgpu

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Tesla K40

August 11, 2019 by hgpu

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

A readahead prefetcher for GPU file system layer

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

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

Hardware/Software Co-Design for Data-Intensive Genomics Workloads

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

Most viewed papers (last 30 days)