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Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

Chenggang Lai

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, MPI, OpenMP, Thesis

December 30, 2018 by hgpu

Software Platform for Hybrid Resource Management of Many-core Accelerators

Taeyoung Kim

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Tags: Computer science, Design space exploration, Intel Xeon Phi, OpenCL, Thesis

December 16, 2018 by hgpu

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Tuowen Zhao, Samuel Williams, Mary Hall, Hans Johansen

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, performance portability, Stencil computation, Tesla P100

December 16, 2018 by hgpu

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 by hgpu

A Fast and Simple Approach to Merge and Merge Sort using Wide Vector Instructions

Alex Watkins, Oded Green

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Tags: Algorithms, Computer science, Intel Xeon Phi, Sorting

December 2, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

Jens Domke, Kazuaki Matsumura, Mohamed Wahib, Haoyu Zhang, Keita Yashima, Toshiki Tsuchikawa, Yohei Tsuji, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, HPC, Intel Xeon Phi, Package

November 11, 2018 by hgpu

OpenCL Performance Prediction using Architecture-Independent Features

Beau Johnston, Greg Falzon, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon R9 290X, AMD Radeon R9 295X2, AMD Radeon RX 480, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, OpenCL, Package, performance portability, Tesla K20, Tesla K40

November 3, 2018 by hgpu

HSTREAM: A directive-based language extension for heterogeneous stream computing

Suejb Memeti, Sabri Pllana

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro P 4000, OpenMP

October 6, 2018 by hgpu

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke

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Tags: Computer science, Deep learning, Intel Xeon Phi, OpenMP, Package, Performance

August 19, 2018 by hgpu

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

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Tags: Astrophysics, Cosmology, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Package, TensorFlow, Tesla P100

August 19, 2018 by hgpu

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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

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

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

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