high performance computing on graphics processing units: hgpu.org

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Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Wookhyun Han, Hwidong Bae, Hyosu Kim, Jiyoen Lee, Insik Shin

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Tags: Computer science, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Package, Performance

August 13, 2014 by hgpu

Optimising Purely Functional GPU Programs (Thesis)

Trevor L. McDonell

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Performance, Tesla T10, Thesis

August 10, 2014 by hgpu

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

A. Gorobets, F.X. Trias, R. Borrell, G. Oyarzun, A. Oliva

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, ATI Radeon HD 7970, Fluid dynamics, Intel Xeon Phi, MPI, Numerical simulation, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX Titan, OpenCL, OpenMP, Performance, Tesla C1060, Tesla C2050

August 10, 2014 by hgpu

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

D. C. Price, M. A. Clark, B. R. Barsdell, R. Babich, L. J. Greenhill

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Tags: Astrophysics, CUDA, Energy-efficient computing, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 780, Package, Performance, Signal processing, Tesla K20

August 1, 2014 by hgpu

Automatic Parallelization of Tiled Stencil Loop Nests on GPUs

Peng Di

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Performance, Tesla C1060, Tesla C2050, Thesis

July 30, 2014 by hgpu

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

Jianbin Fang, Henk Sips, Ana Lucia Varbanescu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, Local memory, nVidia, OpenCL, Performance, Portability, Tesla C1060, Tesla C2050, Tesla K20

July 29, 2014 by jfang

GPU Computing to Improve Game Engine Performance

Abu Asaduzzaman, Hin Y. Lee

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Tags: Computer science, CUDA, Data parallelism, Games, Image processing, nVidia, nVidia GeForce 8800 GTX, Performance

July 28, 2014 by hgpu

Performance-efficient mechanisms for managing irregularity in throughput processors

Minsoo Rhu

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance, Prefetch, Thesis

July 20, 2014 by hgpu

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

Tsutomu Inamoto, Yoshinobu Higami, Shin-ya Kobayashi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance

July 17, 2014 by hgpu

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

David Rohr

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Review, Thesis

July 14, 2014 by hgpu

GPU Techniques Applied to Euler Flow Simulations and Comparison to CPU Performance

Blake Koop

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Tags: CUDA, Fluid dynamics, nVidia, Performance, Tesla C2070

July 14, 2014 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Optimising Purely Functional GPU Programs (Thesis)

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

Automatic Parallelization of Tiled Stencil Loop Nests on GPUs

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

GPU Computing to Improve Game Engine Performance

Performance-efficient mechanisms for managing irregularity in throughput processors

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

GPU Techniques Applied to Euler Flow Simulations and Comparison to CPU Performance

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)