high performance computing on graphics processing units: hgpu.org

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Fast GPGPU Data Rearrangement Kernels using CUDA

Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Performance, Tesla C1060

January 18, 2011 by hgpu

Understanding the design trade-offs among current multicore systems for numerical computations

Seunghwa Kang, David A. Bader, Richard W. Vuduc

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Tesla C1060

January 11, 2011 by hgpu

An events based algorithm for distributing concurrent tasks on multi-core architectures

David W. Holmes, John R. Williams, Peter Tilke

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Tags: Algorithms, Computer science, Performance, Task scheduling

January 11, 2011 by hgpu

Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

Aparna Chandramowlishwarany, Kamesh Madduri, Richard Vuduc

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Tags: Computer science, Fast multipole method, Optimization, Performance

January 11, 2011 by hgpu

High-performance CUDA kernel execution on FPGAs

Alexandros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen Mei

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Tags: Computer science, CUDA, FPGA, Performance, Programming techniques

January 11, 2011 by hgpu

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Rick Weber, Akila Gothandaraman, Robert J. Hinde, Gregory D. Peterson

Tags: ATI, Brook, Computer science, CUDA, FPGA, nVidia, OpenCL, Performance

January 10, 2011 by hgpu

Self-Configuring Applications for Heterogeneous Systems: Program Composition and Optimization Using Cognitive Techniques

M. W. Hall, Y. Gil, R. F. Lucas

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Tags: Compilers, Computer science, FPGA, nVidia, Optimization, Performance

January 8, 2011 by hgpu

FPGA-based acceleration of CHARMM-potential minimization

Bharat Sukhwani, Martin C. Herbordt

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Tags: Chemistry, FPGA, Molecular modeling, nVidia, Performance, Tesla C1060

January 3, 2011 by hgpu

Using Reconfigurable Logic to Optimise GPU Memory Accesses

Ben Cope, Peter Y. K. Cheung, Wayne Luk

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Tags: Computer science, Memory model, nVidia, nVidia GeForce 7900 GTX, Performance

December 27, 2010 by hgpu

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Veerendra Allada, Troy Benjegerdes, Brett Bode

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Presentation, Tesla S1070, Tesla T10

December 27, 2010 by hgpu

Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Nachiket Kapre, Andre DeHon

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Tags: Cell processor, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 9600 GT, Performance

December 26, 2010 by hgpu

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Frederico Pratas, Pedro Trancoso, Alexandros Stama

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Tags: Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Performance, Programming techniques

December 24, 2010 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

Fast GPGPU Data Rearrangement Kernels using CUDA

Understanding the design trade-offs among current multicore systems for numerical computations

An events based algorithm for distributing concurrent tasks on multi-core architectures

Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

High-performance CUDA kernel execution on FPGAs

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Self-Configuring Applications for Heterogeneous Systems: Program Composition and Optimization Using Cognitive Techniques

FPGA-based acceleration of CHARMM-potential minimization

Using Reconfigurable Logic to Optimise GPU Memory Accesses

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

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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