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Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

August Ernstsson, Dalvan Griebler, Christoph Kessler

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Package, performance portability, Tesla K20

December 11, 2022 by hgpu

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

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Clava: C/C++ Source-to-Source Tool based on Clang

Challenges and Opportunities in C/C++ Source-To-Source Compilation

gpuLik

Statistical Computing With Graphics Processing Units

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

See all packages

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high performance computing on graphics processing units: hgpu.org

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

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

Recent source codes

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

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