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Accelerating mahout on heterogeneous clusters using HadoopCL

Xiangyu Li

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Tags: Cluster computing, Computer science, GPU cluster, Heterogeneous systems, Java, Machine learning, MapReduce, nVidia, OpenCL, Tesla K20, Thesis

January 19, 2015 by hgpu

CURFIL: Random Forests for Image Labeling on GPU

Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, Sven Behnke

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 675 M, nVidia GeForce GTX Titan, Package, Tesla K20

January 16, 2015 by hgpu

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

Amlan Chatterjee

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Sorting, Tesla K20, Thesis

January 16, 2015 by hgpu

A Novel Computational Model for GPUs with Applications to Efficient Algorithms

Atsushi Koike, Kunihiko Sadakane

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Tags: Algorithms, Computer science, CUDA, nVidia, Sorting, Tesla C1060, Tesla K20

January 15, 2015 by hgpu

Identification and Elimination of Platform-Specific Code Smells in High Performance Computing Applications

Chunyan Wang, Shoichi Hirasawa, Hiroyuki Takizawa, Hiroaki Kobayashi

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Tags: Computer science, CUDA, nVidia, OpenACC, OpenCL, Tesla C1060, Tesla C2070, Tesla K20

January 15, 2015 by hgpu

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang

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Tags: CNN, CUDA, Image recognition, Neural networks, nVidia, Sensing, Speech recognition, Tesla K20

January 13, 2015 by hgpu

Digital Signal Processing using Stream High Performance Computing: A 512-input Broadband Correlator for Radio Astronomy

J. Kocz, L.J Greenhill, B.R. Barsdell, D. Price, G. Bernardi, S. Bourke, M.A. Clark, J. Craig, M. Dexter, J. Dowell, T. Eftekhari, S. Ellingson, G. Hallinan, J. Hartman, A. Jameson, D. MacMahon, G. Taylor, F. Schinzel, D. Werthimer

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Tags: Astrophysics, CUDA, FPGA, GPU cluster, Heterogeneous systems, Instrumentation and Methods for Astrophysics, nVidia, Package, Signal processing, Tesla K20

January 10, 2015 by hgpu

A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization

Saeed Taheri

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

December 30, 2014 by hgpu

Legion: Programming Distributed Heterogeneous Architectures with Logical Regions

Michael Edward Bauer

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, Package, Tesla K20, Tesla M2090, Thesis

December 22, 2014 by hgpu

PyFAI: a Python library for high performance azimuthal integration on GPU

Jerome Kieffer, Giannis Ashiotis

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Tags: Astrophysics, ATI, ATI FirePro V7800, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, OpenCL, Package, PyOpenCL, Python, Tesla K20

December 22, 2014 by hgpu

Accelerating Ab Initio Nuclear Physics Calculations with GPUs

Hugh Potter, Dossay Oryspayev, Pieter Maris, Masha Sosonkina, James Vary, Sven Binder, Angelo Calci, Joachim Langhammer, Robert Roth, Umit Catalyurek, Erik Saule

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Tags: Computational Physics, CUDA, Nuclear physics, nVidia, Physics, Tesla K20

December 22, 2014 by hgpu

Efficient Workload Balancing on Heterogeneous GPUs using Mixed-Integer Non-Linear Programming

Chih-Sheng Lin, Chih-Wei Hsieh, Hsi-Ya Chang, Pao-Ann Hsiung

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 690, Tesla C2050, Tesla K20

December 20, 2014 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

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

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

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