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Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations

Vignesh T. Ravi, Wenjing Ma, David Chiu, Gagan Agrawal

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX

August 22, 2011 by hgpu

Reusable software components for accelerator-based clusters

M. Mustafa Rafique, Ali R. Butt, Eli Tilevich

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 M GT, Performance, Playstation

August 22, 2011 by hgpu

Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Martin Sandrieser, Siegfried Benkner, Sabri Pllana

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, OpenMP, Presentation

August 22, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

A programming model for GPU-based parallel computing with scalability and abstraction

Balazs Domonkos, Gabor Jakab

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Tags: 3D Graphics and Realism, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, OpenMP, Programming techniques

August 22, 2011 by hgpu

Sponge: portable stream programming on graphics engines

Amir H. Hormati, Mehrzad Samadi, Mark Woh, Trevor Mudge, Scott Mahlke

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization, Performance, Programming Languages

August 21, 2011 by hgpu

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

Jing Guo, Jeyarajan Thiyagalingam, Sven-Bodo Scholz

Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages

August 21, 2011 by hgpu

GRace: a low-overhead mechanism for detecting data races in GPU programs

Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming techniques, Tesla C1060

August 20, 2011 by hgpu

A framework for dynamically instrumenting GPU compute applications within GPU Ocelot

Naila Farooqui, Andrew Kerr, Gregory Diamos, S. Yalamanchili, K. Schwan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Programming Languages, PTX

August 19, 2011 by hgpu

Extending abstract GPU APIs to shared memory

Ferosh Jacob

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, OpenMP, Presentation, Programming Languages, Programming techniques

August 19, 2011 by hgpu

A cluster for CS education in the manycore era

Joel C. Adams, Kathy Hoobeboom, Jonathan Walz

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, nVidia, OpenCL, OpenMP, OpenMPI, Performance

August 19, 2011 by hgpu

Caracal: dynamic translation of runtime environments for GPUs

Rodrigo Dominguez, Dana Schaa, David Kaeli

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Programming Languages, PTX

August 19, 2011 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

Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations

Reusable software components for accelerator-based clusters

Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Accelerating Haskell array codes with multicore GPUs

A programming model for GPU-based parallel computing with scalability and abstraction

Sponge: portable stream programming on graphics engines

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

GRace: a low-overhead mechanism for detecting data races in GPU programs

A framework for dynamically instrumenting GPU compute applications within GPU Ocelot

Extending abstract GPU APIs to shared memory

A cluster for CS education in the manycore era

Caracal: dynamic translation of runtime environments for GPUs

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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