high performance computing on graphics processing units: hgpu.org

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

Input Sensitivity of GPU Program Optimizations

Yixun Liu, Eddy Z. Zhang, Poornima Bhamidipati, Xipeng Shen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, OpenCL, Optimization, Performance, Tesla C1060

October 13, 2011 by hgpu

Developing a High Performance GPGPU Compiler Using Cetus

Yi Yang, Huiyang Zhou

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Tags: ATI, ATI Radeon HD 5850, ATI Radeon HD 5870, Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization

October 13, 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

Optimization strategies for parallel CPU and GPU implementations of a meshfree particle method

Fast Implementation of DGEMM on Fermi GPU

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

An Optimization for Fast Generation of Digital Hologram

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

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

Input Sensitivity of GPU Program Optimizations

Developing a High Performance GPGPU Compiler Using Cetus

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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