high performance computing on graphics processing units: hgpu.org

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Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

MIML Learning with CNNs: Yelp Restaurant Photo Classification

Pulkit Agrawal, Raghav Gupta

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Tags: CNN, Computer science, CUDA, Deep learning, Intel Xeon Phi, nVidia

April 19, 2016 by hgpu

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Yongchao Liu, Srinivas Aluru

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, PTX, Tesla K40

April 19, 2016 by hgpu

Efficiency of general Krylov methods on GPUs – An experimental study

Hartwig Anzt, Jack Dongarra, Moritz Kreutzer, Gerhard Wellein, Martin Kohler

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Package, Sparse matrix, Tesla K40

April 16, 2016 by hgpu

Breadth First Search Vectorization on the Intel Xeon Phi

Mireya Paredes, Graham Riley, Mikel Lujan

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Tags: Algorithms, Computer science, Databases, Intel Xeon Phi, Prefetch

April 14, 2016 by hgpu

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

April 3, 2016 by hgpu

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

Zhaokui Li, Jianbin Fang, Tao Tang, Xuhao Chen, Cheng Chen, Canqun Yang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, Performance

April 3, 2016 by hgpu

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Lukas Einkemmer

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Tags: Computational Physics, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, Mixed precision, nVidia, OpenMP, Tesla K80

March 25, 2016 by hgpu

Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 by hgpu

Programming GPUs with C++14 and Just-In-Time Compilation

Michael Haidl, Bastian Hagedorn, Sergei Gorlatch

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Tags: Compilers, Computer science, CUDA, Intel Xeon Phi, LLVM, nVidia, OpenCL, PTX, Tesla K20

February 10, 2016 by hgpu

Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders

Giuseppe Cerati, Peter Elmer, Steven Lantz, Kevin McDermott, Dan Riley, Matevz Tadel, Peter Wittich, Frank Wurthwein, Avi Yagil

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Tags: High Energy Physics - Experiment, Instrumentation and Detectors, Intel Xeon Phi, Physics

February 2, 2016 by hgpu

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kyungjoo Kim, Sivasankaran Rajamanickam, George Stelle, H. Carter Edwards, Stephen L. Olivier

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Tags: Algorithms, Computer science, Factorization, Intel Xeon Phi, Mathematical Software, Package, Sparse matrix

January 26, 2016 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

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

MIML Learning with CNNs: Yelp Restaurant Photo Classification

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Efficiency of general Krylov methods on GPUs – An experimental study

Breadth First Search Vectorization on the Intel Xeon Phi

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Writing a performance-portable matrix multiplication

Programming GPUs with C++14 and Just-In-Time Compilation

Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

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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