high performance computing on graphics processing units: hgpu.org

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Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

A Performance Analysis Framework for Optimizing OpenCL Applications on FPGAs

Zeke Wang, Bingsheng He, Wei Zhang, Shunning Jiang

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Tags: Computer science, FPGA, OpenCL, Performance

February 4, 2016 by hgpu

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Task scheduling, Tesla K20

February 1, 2016 by hgpu

Parallel Explicit FEM Algorithms Using GPU’s

Seyed Parsa Banihashemi

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Tags: Algorithms, AMD Radeon R9 280X, ATI, ATI Radeon HD 7970, Computer science, FEM, Finite element method, nVidia, nVidia GeForce GTX Titan, OpenCL, Particle swarm optimization, Performance, Tesla M2090, Thesis

January 22, 2016 by hgpu

A framework for efficient execution on GPU and CPU+GPU systems

Jean-Francois Dollinger

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Thesis

January 19, 2016 by hgpu

Performance Analysis of Roberts Edge Detection Using CUDA and OpenGL

Marco Cali, Valeria Di Mauro

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Tags: CUDA, Edge detection, Image processing, nVidia, nVidia GeForce GTX 845 M, OpenGL, Performance

January 16, 2016 by hgpu

A Workload Balanced MapReduce Framework on GPU Platforms

Yue Zhang

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Tags: Benchmarking, Computer science, CUDA, MapReduce, nVidia, Performance, Tesla K40, Thesis

January 12, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

Study of basic vector operations on Intel Xeon Phi and NVIDIA Tesla using OpenCL

Edoardo Coronado, Guillermo Indalecio, Antonio Garcia-Loureiro

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Performance, Tesla S2050

December 31, 2015 by hgpu

A Comparison of the performance of HPC Accelerators

Xiao Li

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Tags: AMD FirePro W9100, ATI, Computer science, nVidia, OpenCL, Performance, Tesla K40, Thesis

December 31, 2015 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

Writing a performance-portable matrix multiplication

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Asynchronous Methods for Deep Reinforcement Learning

A Performance Analysis Framework for Optimizing OpenCL Applications on FPGAs

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Parallel Explicit FEM Algorithms Using GPU’s

A framework for efficient execution on GPU and CPU+GPU systems

Performance Analysis of Roberts Edge Detection Using CUDA and OpenGL

A Workload Balanced MapReduce Framework on GPU Platforms

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Study of basic vector operations on Intel Xeon Phi and NVIDIA Tesla using OpenCL

A Comparison of the performance of HPC Accelerators

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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