high performance computing on graphics processing units: hgpu.org

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Persistent RNNs: Stashing Recurrent Weights On-Chip

Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Speech recognition

June 30, 2016 by hgpu

Parallel and Distributed Deep Learning

Vishakh Hegde, Sheema Usmani

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Tags: Caffe, CNN, Computer science, CUDA, Deep learning, nVidia, nVidia GRID K520

June 28, 2016 by hgpu

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Weifeng Liu, Ang Li, Jonathan Hogg, Iain S. Duff, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 Fury X, ATI, Computer science, CUDA, Linear Algebra, nVidia GeForce GTX Titan X, OpenCL, Package, Sparse direct solvers, Tesla K40

June 28, 2016 by hgpu

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Daniel Chavez

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Tags: Computer science, CUDA, nVidia, nVidia Quadro 1000 M, OpenCL, Pthreads, Thesis

June 28, 2016 by hgpu

Accelerating High-Throughput Computing through OpenCL

Andrei Dafinoiu, Joshua Higgins, Violeta Holmes

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Tags: ATI, ATI Radeon HD 6570, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 970, OpenCL, Tesla C2050

June 28, 2016 by hgpu

Efficient and portable multi-tasking for heterogeneous systems

Christos Margiolas

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Tags: ATI, ATI Radeon HD 5970, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, OpenCL, Tesla K20, Thesis, Virtualization

June 22, 2016 by hgpu

Efficient and High-quality Sparse Graph Coloring on the GPU

Xuhao Chen, Pingfan Li, Canqun Yang

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

June 22, 2016 by hgpu

Tensor Contractions with Extended BLAS Kernels on CPU and GPU

Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka

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

June 22, 2016 by hgpu

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

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Tags: CNN, Computer science, CUDA, Deep learning, FPGA, MapReduce, Neural networks, nVidia, OpenCL, Tesla K40

June 22, 2016 by hgpu

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

Kevin Andryc, Tedy Thomas, Russell Tessier

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Tags: Benchmarking, Computer science, CUDA, DSP, FPGA

June 22, 2016 by hgpu

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini

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Tags: Computer science, Computer vision, Deep learning, Hardware Architecture, Neural networks

June 21, 2016 by hgpu

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

Suejb Memeti, Sabri Pllana

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning, Optimization

June 21, 2016 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

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

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high performance computing on graphics processing units: hgpu.org

Persistent RNNs: Stashing Recurrent Weights On-Chip

Parallel and Distributed Deep Learning

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Accelerating High-Throughput Computing through OpenCL

Efficient and portable multi-tasking for heterogeneous systems

Efficient and High-quality Sparse Graph Coloring on the GPU

Tensor Contractions with Extended BLAS Kernels on CPU and GPU

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

Recent source codes

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

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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