high performance computing on graphics processing units: hgpu.org

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Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Chapa Martell Mario Alberto, Hiroyuki Sato

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Tags: Computer science, nVidia, nVidia GeForce GTX 660, OpenCL, Performance

January 13, 2015 by hgpu

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Satoshi Okamoto, Yasuaki Ito, Koji Nakano, Jacir L. Bordim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 780 Ti, Performance, PTX

January 13, 2015 by hgpu

Performance and Power Comparisons Between Nvidia and ATI GPUs

Ying Zhang, Lu Peng, Bin Li, Jih-Kwon Peir, Jianmin Chen

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

January 8, 2015 by hgpu

Customization of OpenCL Applications for Efficient Task Mapping under Heterogeneous Platform Constraints

Edoardo Paone, Francesco Robino, Gianluca Palermo, Vittorio Zaccaria, Ingo Sander, Cristina Silvano

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro NVS 300, OpenCL, Performance

January 2, 2015 by hgpu

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Vajira Thambawita, Roshan Ragel, Dhammika Elkaduwe

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 740 M, Performance, String matching

December 30, 2014 by hgpu

Automatic Tuning of Local Memory Use on GPGPUs

Tianyi David Han, Tarek S. Abdelrahman

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Tags: Benchmarking, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla M2090

December 26, 2014 by hgpu

Accelerating Correlation Power Analysis Using Graphics Processing Units

Hasindu Gamaarachchi, Roshan Ragel, Darshana Jayasinghe

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Tags: AES, Algorithms, Computer science, CUDA, nVidia, Performance, Security, Tesla C2075

December 26, 2014 by hgpu

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Marvin Damschen, Christian Plessl

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Source codes

Tags: Computer science, Intel Xeon Phi, Package, Performance

December 15, 2014 by hgpu

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Jose Ritomar Carneiro Torquato, Esteban Walter Gonzalez Clua

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance, Tesla K10, Tesla K20

December 14, 2014 by hgpu

Lattice QCD with Domain Decomposition on Intel Xeon Phi Co-Processors

Simon Heybrock, Balint Joo, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey

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Tags: Algorithms, Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Performance, Physics, QCD

December 9, 2014 by hgpu

Improving GPU Performance by Regrouping CPU-Memory Data

Deepthi Gummadi

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Tags: ARM, Computer science, CUDA, nVidia, Performance, Tesla C2075, Tesla K20, Thesis

November 25, 2014 by hgpu

A Self-Optimizing Framework for Developing Metrology Software on Massive Parallel Processor Architectures

T. Beier

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Tags: Algorithms, ATI, ATI Radeon HD 7750, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Performance

November 25, 2014 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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high performance computing on graphics processing units: hgpu.org

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Performance and Power Comparisons Between Nvidia and ATI GPUs

Customization of OpenCL Applications for Efficient Task Mapping under Heterogeneous Platform Constraints

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Automatic Tuning of Local Memory Use on GPGPUs

Accelerating Correlation Power Analysis Using Graphics Processing Units

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Lattice QCD with Domain Decomposition on Intel Xeon Phi Co-Processors

Improving GPU Performance by Regrouping CPU-Memory Data

A Self-Optimizing Framework for Developing Metrology Software on Massive Parallel Processor Architectures

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

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