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Capturing the Memory Topology of GPUs

Dominik Größler

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce 840 M, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Performance, PTX, Tesla K20, Tesla V100, Thesis

November 13, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Raúl Nozal González

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Tags: AMD Radeon RX 5700 XT, ATI, Computer science, Heterogeneous systems, HPC, nVidia, nVidia GeForce GTX 950, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, SYCL, Tesla K20, Thesis

April 10, 2022 by hgpu

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, OpenCL, Tesla K20, Thesis

February 13, 2022 by hgpu

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Bo Qiao

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Tags: Code generation, Computer science, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce RTX 2080, OpenCL, Package, performance portability, Tesla K20, Thesis

January 2, 2022 by hgpu

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA

Javier Prades Gasulla

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Tags: Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Tesla K40, Tesla P100, Thesis, Virtualization

June 27, 2021 by hgpu

Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

Borja Pérez, E. Stafford, J.L. Bosque, R. Beivide

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Tags: Algorithms, Computer science, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Tesla K20

June 27, 2021 by hgpu

Using Workload Characterization to Guide High Performance Graph Processing

Mohamed W. Hassan

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, nVidia, OpenCL, Optimization, Tesla K20, Thesis

May 30, 2021 by hgpu

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Patrick Tobias Haft

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Tags: Code generation, Computational biology, Computer science, Differential equations, FPGA, nVidia, nVidia GeForce GTX 980 Ti, ODEs, OpenCL, Optimization, Ordinary differential equations, Tesla K20, Thesis

May 16, 2021 by hgpu

Large Scale GPU Based Simulations of Turbulent Bubbly Flow in a Square Duct

Purushotam Kumar, Surya Pratap Vanka

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, Tesla C2075, Tesla K20, Tesla P100

April 11, 2021 by hgpu

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Tomasz Boiński, Paweł Czarnul

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Tesla K20

February 14, 2021 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Capturing the Memory Topology of GPUs

Benchmarking optimization algorithms for auto-tuning GPU kernels

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems

Using Workload Characterization to Guide High Performance Graph Processing

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Large Scale GPU Based Simulations of Turbulent Bubbly Flow in a Square Duct

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Recent source codes

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

SYCL-DNN: a library implementing neural network algorithms written using SYCL

SW#SYCL

Flink-TornadoVM-Artifact

StateMover: a checkpoint-based debugging framework for FPGAs

Kernel Tuner

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

NaturalCC: An Open-Source Toolkit for Code Intelligence

mt4g: Memory Topology for GPUs

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