high performance computing on graphics processing units: hgpu.org

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Mint: realizing CUDA performance in 3D stencil methods with annotated C

Didem Unat, Xing Cai, Scott B. Baden

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, OpenMP, Optimization, Package, Performance, Programming Languages, Tesla C1060

September 21, 2011 by hgpu

SRAM-DRAM hybrid memory with applications to efficient register files in fine-grained multi-threading

Wing-kei S. Yu, Ruirui Huang, Sarah Q. Xu, Sung-En Wang, Edwin Kan, G. Edward Suh

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Tags: Benchmarking, Computer science, Memory, nVidia, Performance

September 20, 2011 by hgpu

Brief announcement: better speedups for parallel max-flow

George Constantin Caragea, Uzi Vishkin

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Tags: Algorithms, ASIC, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Performance

September 20, 2011 by hgpu

Hermes: an integrated CPU/GPU microarchitecture for IP routing

Yuhao Zhu, Yangdong Deng, Yubei Chen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Software router

September 20, 2011 by hgpu

CuMAPz: a tool to analyze memory access patterns in CUDA

Yooseong Kim, Aviral Shrivastava

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Tags: Analytical model, Benchmarking, Computer science, CUDA, Memory, nVidia, Performance, Tesla C1060

September 20, 2011 by hgpu

Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar

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Tags: Algorithms, Cloud, Computer science, CUDA, nVidia, Operating systems, Performance, Tesla C2050, Virtualization

September 20, 2011 by hgpu

A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

Ping Guo, He Huang, Qichang Chen, Liqiang Wang, En-Jui Lee, Po Chen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Software Engineering, Sparse matrix

September 19, 2011 by hgpu

A GPU-tailored approach for training kernelized SVMs

Andrew Cotter, Nathan Srebro, Joseph Keshet

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Tags: Algorithms, Clustering, Computer science, nVidia, Optimization, Package, Performance, Programming techniques, Tesla C1060

September 19, 2011 by hgpu

Using many-core hardware to correlate radio astronomy signals

Rob V. van Nieuwpoort, John W. Romein

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Tags: Algorithms, Astrophysics, ATI, ATI CAL, ATI Radeon HD 4870, ATI Stream, Cell processor, CUDA, nVidia, Performance, Physics, Signal processing, Tesla C1060

September 19, 2011 by hgpu

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Mark Joselli, Marcelo Zamith, Esteban Clua, Anselmo Montenegro, Regina Leal-Toledo, Aura Conci, Paulo Pagliosa, Luis Valente, Bruno Feijo

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce 8200 M, nVidia GeForce 8400 GS, nVidia GeForce 8800 GTS, Performance, Task scheduling

September 19, 2011 by hgpu

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Amol Ghoting, Prabhanjan Kambadur, Edwin Pednault, Ramakrishnan Kannan

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Tags: Algorithms, Computer science, Data mining, Machine learning, MapReduce, MPI, OpenCL, Performance, Software Engineering

September 16, 2011 by hgpu

Programmable and Scalable Architecture for Graphics Processing Units

Carlos S. de La Lama, Pekka Jaaskelainen, Jarmo Takala

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Tags: Computer science, GLSL, nVidia, nVidia GeForce 9400 M, OpenCL, OpenGL, Performance, Programming techniques

September 16, 2011 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Mint: realizing CUDA performance in 3D stencil methods with annotated C

SRAM-DRAM hybrid memory with applications to efficient register files in fine-grained multi-threading

Brief announcement: better speedups for parallel max-flow

Hermes: an integrated CPU/GPU microarchitecture for IP routing

CuMAPz: a tool to analyze memory access patterns in CUDA

A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

A GPU-tailored approach for training kernelized SVMs

Using many-core hardware to correlate radio astronomy signals

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Programmable and Scalable Architecture for Graphics Processing Units

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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