high performance computing on graphics processing units: hgpu.org

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha Shet G, Bharat Kaul, Balint Joo, Pradeep Dubey

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Tags: Cluster computing, Computer science, FFT, Intel Xeon Phi, Performance, QCD

August 15, 2014 by hgpu

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Thomas R. W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla K20

August 15, 2014 by hgpu

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

A. Gorobets, F.X. Trias, R. Borrell, G. Oyarzun, A. Oliva

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, ATI Radeon HD 7970, Fluid dynamics, Intel Xeon Phi, MPI, Numerical simulation, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX Titan, OpenCL, OpenMP, Performance, Tesla C1060, Tesla C2050

August 10, 2014 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

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Enabling Efficient Use of MPI and PGAS Programming Models on Heterogeneous Clusters with High Performance Interconnects

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Performance analysis of a 240 thread tournament level MCTS Go program on the Intel Xeon Phi

Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi

HISQ inverter on Intel Xeon Phi and NVIDIA GPUs

A uniform approach for programming distributed heterogeneous computing systems

Performance Portability Study of Linear Algebra Kernels in OpenCL

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

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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