high performance computing on graphics processing units: hgpu.org

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Accelerated dimension-independent adaptive Metropolis

Yuxin Chen, David Keyes, Kody J.H. Law, Hatem Ltaief

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Tags: CUDA, nVidia, Probability, Statistics, Tesla K40

June 19, 2015 by hgpu

Exploring the Suitability of Remote GPGPU Virtualization for the OpenACC Programming Model Using rCUDA

Adrian Castello, Rafael Mayo, Enrique S. Quintana-Orti, Antonio J. Pena, Pavan Balaji

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Tesla C2050, Tesla K40, Virtualization

June 17, 2015 by hgpu

Layered Interpretation of Street View Images

Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam, Oncel Tuzel

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Tags: Algorithms, Computer science, Computer vision, CUDA, Neural networks, nVidia, Robotics, Tesla K40, Vehicular traffic

June 17, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

Type-safe Runtime Code Generation: Accelerate to LLVM

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, Ryan R. Newton

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Sparse matrix, Tesla K40

June 14, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla K40

June 7, 2015 by hgpu

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

Michael Vollmer, Bo Joel Svensson, Eric Holk, Ryan Newton

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Tags: Computer science, CUDA, nVidia, Performance, Search strategies, Tesla K40

June 7, 2015 by hgpu

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, OpenCL, Tesla K40

June 5, 2015 by hgpu

Accelerated Nodal Discontinuous Galerkin Simulations for Reverse Time Migration with Large Clusters

Axel Modave, Amik St-Cyr, Wim A. Mulder, Tim Warburton

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, CUDA, Geophysics, MPI, nVidia, nVidia GeForce GTX 980, OpenCL, Physics, Tesla K20, Tesla K40

June 5, 2015 by hgpu

Genetically Improved BarraCUDA

W. B. Langdon, Brian Yee Hong Lam

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Tags: Algorithms, Benchmarking, Biology, CUDA, Genomics, nVidia, nVidia GeForce GT 730, Package, Tesla K20, Tesla K40, Tesla K80

June 1, 2015 by hgpu

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G. Dreslinski, Jason Mars, Lingjia Tang

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Tags: CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K40

May 25, 2015 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

Accelerated dimension-independent adaptive Metropolis

Exploring the Suitability of Remote GPGPU Virtualization for the OpenACC Programming Model Using rCUDA

Layered Interpretation of Street View Images

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

Type-safe Runtime Code Generation: Accelerate to LLVM

Automatic Selection of Sparse Matrix Representation on GPUs

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Accelerated Nodal Discontinuous Galerkin Simulations for Reverse Time Migration with Large Clusters

Genetically Improved BarraCUDA

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

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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