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Implementation of Parallel Simplified Swarm Optimization in CUDA

Wei-Chang Yeh, Zhenyao Liu, Shi-Yi Tan, Shang-Ke Huang

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, Particle swarm optimization

October 10, 2021 by hgpu

Performance analysis and optimization of highly diverging algorithms on GPUs

Hendrik Schwanekamp

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Tags: CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080, Performance, Physics

May 2, 2021 by hgpu

INSTA-YOLO: Real-Time Instance Segmentation

Eslam Mohamed, Abdelrahman Shaker, Hazem Rashed, Ahmad El-Sallab, Mayada Hadhoud

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Tags: Deep learning, Image processing, nVidia, nVidia GeForce GTX 1080

February 21, 2021 by hgpu

Cross-platform programming model for many-core lattice Boltzmann simulations

Jonas Latt, Christophe Coreixas, Joël Beny

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Tags: Algorithms, cfd, Computational Physics, CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 1080, nVidia GeForce RTX 2080, Physics

October 25, 2020 by hgpu

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Rohan Mahapatra

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, Thesis

September 20, 2020 by hgpu

Parallel acceleration of CPU and GPU range queries over large data sets

Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

August 9, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

AvA: Accelerated Virtualization of Accelerators

Hangchen Yu, Arthur Michener Peters, Amogh Akshintala, Christopher J. Rossbach

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Tags: AMD Radeon RX 580, AMD RX Vega 64, ATI, Cloud, Code generation, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Virtualization

March 1, 2020 by hgpu

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Akrem Benatia, Weixing Ji, Yizhuo Wang, Feng Shi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Sparse matrix

January 5, 2020 by hgpu

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Tim Besard

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Thesis

December 29, 2019 by hgpu

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

Implementation of high speed hash function Keccak on GPU

Thuong Nguyen Dat, Keisuke Iwai, Takashi Matsubara, Takakazu Kurokawa

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 1080, Security

July 14, 2019 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

Implementation of Parallel Simplified Swarm Optimization in CUDA

Performance analysis and optimization of highly diverging algorithms on GPUs

INSTA-YOLO: Real-Time Instance Segmentation

Cross-platform programming model for many-core lattice Boltzmann simulations

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Parallel acceleration of CPU and GPU range queries over large data sets

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Implementation of high speed hash function Keccak on GPU

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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