high performance computing on graphics processing units: hgpu.org

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An initial performance review of software components for a heterogeneous computing platform

Ivan Svogor

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Tags: ATI, ATI Radeon HD 7750, Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, OpenCL, Performance, Robotics

January 4, 2017 by hgpu

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Performance, Tesla M40, Torch

December 26, 2016 by hgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, Computer science, CUDA, Factorization, FPGA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Performance, Tesla C2050

December 17, 2016 by hgpu

Automating the Last-Mile for High Performance Dense Linear Algebra

Richard Michael Veras, Tze Meng Low, Tyler Michael Smith, Robert van de Geijn, Franz Franchetti

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Tags: Code generation, Computer science, Intel Xeon Phi, Linear Algebra, Performance

December 14, 2016 by hgpu

Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform

Qingyi Pan, Xiaoying Wang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, OpenMP, Performance

December 10, 2016 by hgpu

Adaptive Work-Efficient Connected Components on the GPU

Michael Sutton, Tal Ben-Nun, Amnon Barak, Sreepathi Pai, Keshav Pingali

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Tags: Computer science, nVidia, Performance, Tesla M60

December 10, 2016 by hgpu

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Marcos Amaris, Raphael Y. de Camargo, Mohamed Dyab, Alfredo Goldman, Denis Trystram

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Tags: Analytical model, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 970, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X, nVidia Quadro K5200, Package, Performance, Tesla K20, Tesla K40

December 6, 2016 by hgpu

OpenACC cache Directive: Opportunities and Optimizations

Ahmad Lashgar, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Tesla K20

December 3, 2016 by hgpu

Should I use TensorFlow?

Martin Schrimpf

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Python, TensorFlow

December 3, 2016 by hgpu

Hardware thread reordering to boost OpenCL throughput on FPGAs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Computer science, FPGA, Hardware, OpenCL, Performance

November 30, 2016 by hgpu

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Computer science, CUDA, Deep learning, Intel Xeon Phi, NLP, nVidia, nVidia GeForce GTX Titan X, Performance, Tesla K40

November 30, 2016 by hgpu

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

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high performance computing on graphics processing units: hgpu.org

An initial performance review of software components for a heterogeneous computing platform

Language Modeling with Gated Convolutional Networks

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Automating the Last-Mile for High Performance Dense Linear Algebra

Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform

Adaptive Work-Efficient Connected Components on the GPU

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

OpenACC cache Directive: Opportunities and Optimizations

Should I use TensorFlow?

Hardware thread reordering to boost OpenCL throughput on FPGAs

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

LC Framework

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