high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Computer science, Intel, Intel Phi, Performance

July 13, 2013 by hgpu

SIMD Divergence Optimization through Intra-Warp Compaction

Aniruddha S. Vaidya, Anahita Shayesteh, Dong Hyuk Woo, Roy Saharoy, Mani Azimi

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Tags: Computer science, Hardware Architecture, OpenCL, OpenGL, Performance

July 12, 2013 by hgpu

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

Nouredine Melab, The van Luong, Karima Boufaras, El-Ghazali Talbi

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Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 480, Package, Performance, Search

July 8, 2013 by hgpu

Optimize Overall System Performance Through Workload Sequencing for GPUs Data Offloading

Wei Liu, Jian-Jia Chen, Qingxu Deng, Tei-Wei Kuo, Xue Liu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 630 M, Performance

July 5, 2013 by hgpu

Preliminary Experiences with the Uintah Framework on Intel Xeon Phi and Stampede

Qingyu Meng, Alan Humphrey, John Schmidt, Martin Berzins

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Tags: Computer science, Heterogeneous systems, Intel, Intel Phi, Performance

July 2, 2013 by hgpu

GPU-CC: a Reconfigurable GPU Architecture with Communicating Cores

Gert-Jan van den Braak, Henk Corporaal

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 470, Performance

June 28, 2013 by hgpu

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

Paul Rosen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Wavelet

June 24, 2013 by hgpu

Provably Efficient GPU Algorithms

Nodari Sitchinava, Volker Weichert

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Sorting

June 24, 2013 by hgpu

Exploring GPGPUs Workload Characteristics and Power Consumption

Sohan Lal, Jan Lucas, Mauricio Alvarez-Mesa, Ahmed Elhossini, Ben Juurlink

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Tags: Computer science, CUDA, Energy-efficient computing, GPGPU-sim, nVidia, nVidia GeForce GTX 580, Performance

June 22, 2013 by hgpu

Auto-Tunning of Data Communication on Heterogeneous Systems

Marc Jorda, Ivan Tanasic, Javier Cabezas, Lluis Vilanova, Isaac Gelado, Nacho Navarro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2050, Tesla C2070, Tesla M2090

June 17, 2013 by hgpu

Performance Analysis on Energy Efficient High-Performance Architectures

Roman Iakymchuk, Francois Trahay

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Tags: ARM, Computer science, CUDA, Energy-efficient computing, nVidia, Performance

June 16, 2013 by hgpu

A comprehensive study of Dynamic Memory Management in OpenCL kernels

Roy Spliet

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Tags: Algorithms, ATI, Computer science, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX 480, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C2050, Tesla K20, Thesis

June 6, 2013 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

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

SIMD Divergence Optimization through Intra-Warp Compaction

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

Optimize Overall System Performance Through Workload Sequencing for GPUs Data Offloading

Preliminary Experiences with the Uintah Framework on Intel Xeon Phi and Stampede

GPU-CC: a Reconfigurable GPU Architecture with Communicating Cores

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

Provably Efficient GPU Algorithms

Exploring GPGPUs Workload Characteristics and Power Consumption

Auto-Tunning of Data Communication on Heterogeneous Systems

Performance Analysis on Energy Efficient High-Performance Architectures

A comprehensive study of Dynamic Memory Management in OpenCL kernels

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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