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Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Code Optimization and Scaling of the Astrophysics Software Gadget on Intel Xeon Phi

P. Borovska, D. Ivanova

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Tags: Astrophysics, Intel Xeon Phi, MPI, N-body simulation, Performance

March 6, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

Performance Analysis for GPU-based Ray-triangle Algorithms

Juan J. Jimenez, Carlos J. Ogayar, Jose M. Noguera, Felix Paulano

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Performance, Raytracing

March 6, 2014 by hgpu

Increasing predictability of GPU’s

David Defour

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

March 3, 2014 by hgpu

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

Zheng Wang, Daniel Powell, Bjorn Franke, Michael O'Boyle

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

February 27, 2014 by hgpu

A Similarity-Based Analysis Tool for Scientific Application Porting

Wei Ding

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenMP, Performance, Portability, Tesla C2070, Thesis

February 17, 2014 by hgpu

LDetector: A Low Overhead Race Detector For GPU Programs

Pengcheng Li, Chen Ding, Xiaoyu Hu, Tolga Soyata

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20

February 16, 2014 by hgpu

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Sadaf Alam, Ugo Varetto

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K20, Tesla M2090

February 15, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Source codes

Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Multi-tier Dynamic Vectorization for Translating GPU Optimizations into CPU Performance

Hee-Seok Kim, Izzat El Hajj, John A. Stratton, Wen-Mei W. Hwu

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Tags: AMD, Computer science, nVidia, OpenCL, Performance

February 12, 2014 by hgpu

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Vivek K. Pallipuram

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Performance, Tesla K20, Tesla M2075, Thesis

February 11, 2014 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Code Optimization and Scaling of the Astrophysics Software Gadget on Intel Xeon Phi

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

Performance Analysis for GPU-based Ray-triangle Algorithms

Increasing predictability of GPU’s

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

A Similarity-Based Analysis Tool for Scientific Application Porting

LDetector: A Low Overhead Race Detector For GPU Programs

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Multi-tier Dynamic Vectorization for Translating GPU Optimizations into CPU Performance

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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