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CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

Michael Bauer, Henry Cook, Brucek Khailany

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Tags: Benchmarking, Computer science, CUDA, Memory model, nVidia, Optimization, Package, Performance, Tesla C2050

October 14, 2011 by hgpu

OptiML: An implicitly parallel domain-specific language for machine learning

Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla C2050

October 14, 2011 by hgpu

Liszt: A Domain Specific Language for Building Portable Mesh-based PDE Solvers

Zachary DeVito, Niels Joubert, Francisco Palacios, Stephen Oakley, Montserrat Medina, Mike Barrientos, Erich Elsen, Frank Ham, Alex Aiken, Karthik Duraisamy, Eric Darve, Juan Alonso, Pat Hanrahan

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, Partial differential equations, PDEs, Performance, Programming Languages, Tesla C2050

October 14, 2011 by hgpu

A Heterogeneous Parallel Framework for Domain-Specific Languages

Kevin J. Brown, Arvind K. Sujeeth, HyoukJoong Lee, Tiark Rompf, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Programming techniques, Tesla C2050

October 14, 2011 by hgpu

Optimizing a High Energy Physics (HEP) Toolkit on Heterogeneous Architectures

Yngve Sneen Lindal

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Tags: ATI, ATI Radeon HD 5870, Heterogeneous systems, High Energy Physics - Experiment, nVidia, nVidia GeForce GTX 470, OpenCL, Optimization, Physics, Review, Tesla C2050, Thesis

October 12, 2011 by hgpu

Static Compilation Analysis for Host-Accelerator Communication Optimization

Mehdi Amini, Fabien Coelho, Francois Irigoin, Ronan Keryell

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Tags: Benchmarking, Computer science, CUDA, Numerical simulation, nVidia, OpenMP, Optimization, Package, Tesla C2050

October 11, 2011 by hgpu

Evaluation of GPU Architectures Using Spiking Neural Networks

Vivek K. Pallipuram, Mohammad A. Bhuiyan, Melissa C. Smith

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Tags: ATI, ATI Radeon HD 5870, Computer science, Neural networks, nVidia, OpenCL, Optimization, Performance, Presentation, Tesla C2050

October 10, 2011 by hgpu

Data-Parallel Construction of delta_N-Nets with Maximum Dispersion

Peter Zinterhof

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Tags: Algorithms, Computational Complexity, Computer science, Nearest neighbour, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 480, OpenCL, OpenMPI, Search, Tesla C2050

October 10, 2011 by hgpu

Non-recursive beam search on GPU for formal concept analysis

W. B. Langdon, Shin Yoo, Mark Harman

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Tesla C2050

October 7, 2011 by hgpu

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Andrew Nere, Atif Hashmi, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Image recognition, Neural networks, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Tesla C2050

October 5, 2011 by hgpu

Performance Analysis and Optimisation of the OP2 Framework on Many-core Architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 260, OpenMP, Partial differential equations, PDEs, Tesla C2050

October 5, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

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high performance computing on graphics processing units: hgpu.org

CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

OptiML: An implicitly parallel domain-specific language for machine learning

Liszt: A Domain Specific Language for Building Portable Mesh-based PDE Solvers

A Heterogeneous Parallel Framework for Domain-Specific Languages

Optimizing a High Energy Physics (HEP) Toolkit on Heterogeneous Architectures

Static Compilation Analysis for Host-Accelerator Communication Optimization

Evaluation of GPU Architectures Using Spiking Neural Networks

Data-Parallel Construction of delta_N-Nets with Maximum Dispersion

Non-recursive beam search on GPU for formal concept analysis

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Performance Analysis and Optimisation of the OP2 Framework on Many-core Architectures

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Recent source codes

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

ROCm's implementation of Gromacs

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

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