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Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems

Mayank Daga

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Heterogeneous systems, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Tesla C2050, Thesis

September 30, 2011 by hgpu

Possible planet-forming regions on submillimetre images

Zsolt Regaly, A. Juhasz, Zs. Sandor, C. P. Dullemond

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Tags: Astrophysics, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, Package, Solar and Stellar Astrophysics

September 29, 2011 by hgpu

Generating GPU Code from a High-level Representation for Image Processing Kernels

Richard Membarth, Anton Lokhmotov, Jurgen Teich

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Optimization, Tesla C2050

September 26, 2011 by hgpu

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

Pablo Lamilla Alvarez, Shinichi Yamagiwa, Masahiro Arai, Koichi Wada

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, OpenGL, Performance

September 26, 2011 by hgpu

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Jens Breitbart, Claudia Fohry

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Tags: Cell processor, Computer science, Distributed computing, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Playstation

September 24, 2011 by hgpu

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

Deepthi Nandakumar

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Thesis

September 24, 2011 by hgpu

CU2CL: A CUDA-to-OpenCL Translator for Multi-and Many-core Architectures

Gabriel Martinez, Wu-chun Feng, Mark Gardner

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 280, OpenCL

September 24, 2011 by hgpu

Embedding OpenCL in C++ for Expressive GPU Programming

Orion Lawlor

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Tags: ATI, ATI Radeon HD 6850, Benchmarking, Code generation, Computer science, GLSL, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 460 M, nVidia GeForce GTX 580, OpenCL, OpenGL, Package

September 23, 2011 by hgpu

Colored stochastic shadow maps

Morgan McGuire, Eric Enderton

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Image generation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Presentation

September 23, 2011 by hgpu

A case for neuromorphic ISAs

Atif Hashmi, Andrew Nere, James Jamal Thomas, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Java, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Performance

September 22, 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

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Florian Worsdorfer, Florian Stock, Eduardo Bayro-Corrochano, Dietmar Hildenbrand

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization, Package, Performance

September 16, 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

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high performance computing on graphics processing units: hgpu.org

Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems

Possible planet-forming regions on submillimetre images

Generating GPU Code from a High-level Representation for Image Processing Kernels

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

CU2CL: A CUDA-to-OpenCL Translator for Multi-and Many-core Architectures

Embedding OpenCL in C++ for Expressive GPU Programming

Colored stochastic shadow maps

A case for neuromorphic ISAs

Brief announcement: better speedups for parallel max-flow

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

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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