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The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Tobias Grosser, Sven Verdoolaege, Albert Cohen, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia Quadro NVS 5200 M, PTX

July 31, 2013 by hgpu

Counting and Occurrence Sort for GPUs using an Embedded Language

Josef Svenningsson, Bo Joel Svensson, Mary Sheeran

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Tags: Algorithms, Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 670, Package, Sorting

July 30, 2013 by hgpu

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

Marek Blazewicz, Ian Hinder, David M. Koppelman, Steven R. Brandt, Milosz Ciznicki, Michal Kierzynka, Frank Loffler, Erik Schnetter, Jian Tao

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Tags: Code generation, Computational Physics, CUDA, Differential equations, Finite difference, MPI, nVidia, OpenMP, Partial differential equations, PDEs, Physics, Tesla M2050

July 25, 2013 by hgpu

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Dmitry Mikushin, Nikolay Likhogrud, Eddy Zheng Zhang, Christopher Bergstrom

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Tags: Code generation, Compilers, Computer science, CUDA, Fortran, nVidia, OpenACC, Package, PTX, Tesla K20

July 15, 2013 by hgpu

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

George Zaki

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Tags: Algorithms, Code generation, Computational Engineering, CUDA, Data parallelism, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, Signal processing, Thesis

July 12, 2013 by hgpu

Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory

Roshan Dathathri, Chandan Reddy, Thejas Ramashekar, Uday Bondhugula

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Tags: ATI, ATI FirePro V4800, ATI Radeon HD 6550, Code generation, Computer science, Heterogeneous systems, nVidia, Tesla C2050

July 1, 2013 by hgpu

Exploiting multi-level parallelism in streaming applications for heterogeneous platforms with GPUs

Ana Balevic

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Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla C2050, Thesis

July 1, 2013 by hgpu

Stencil and Lattice Structures for Field Equation Model Simulations on GPUs

D.P. Playne, K.A. Hawick

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Tags: Code generation, Computer science, CUDA, Field equations, nVidia, nVidia GeForce GTX 580

May 25, 2013 by hgpu

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K. A. Hawick, D. P. Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

May 25, 2013 by hgpu

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Jiri Filipovic, Matus Madzin, Jan Fousek, Ludek Matyska

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Tags: BLAS, Code generation, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 480

May 7, 2013 by hgpu

Automatic Compilation for Heterogeneous Architectures with Single Assignment C

Miguel Sousa Diogo

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Thesis

April 30, 2013 by hgpu

Split tiling for GPUs: automatic parallelization using trapezoidal tiles

Tobias Grosser, Albert Cohen, Paul H J Kelly, J. Ramanujam, P. Sadayappan, Sven Verdoolaege

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia Quadro NVS 5200 M, Package, Performance

April 29, 2013 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

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Counting and Occurrence Sort for GPUs using an Embedded Language

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory

Exploiting multi-level parallelism in streaming applications for heterogeneous platforms with GPUs

Stencil and Lattice Structures for Field Equation Model Simulations on GPUs

On-Demand Generating and Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Automatic Compilation for Heterogeneous Architectures with Single Assignment C

Split tiling for GPUs: automatic parallelization using trapezoidal tiles

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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