Compare and test GPU programming frameworks
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Updated
Jul 20, 2025 - C++
Compare and test GPU programming frameworks
CHARM: Composing Heterogeneous Accelerators on Heterogeneous SoC Architecture
Concurrent CPU-GPU Programming using Task Models
a unified cross-architecture heterogeneous CFD solver
AIM: Accelerating Arbitrary-precision Integer Multiplication on Heterogeneous Reconfigurable Computing Platform Versal ACAP (Full Paper accepted to ICCAD2023)!
Usability and Performance in Heterogeneous Computing. Official EngineCL repository. Peer-reviewed (FGCS).
Development and simulation framework for Application Specific Vector Processor
Multi-GPU & CPU OpenCL kernel executor with load-balancing as if there is one big GPU.
HCGrid is a convolution-based gridding framework for radio astronomy on hybrid computing environments
SYCL accelerated Binary Merklization using SHA1, SHA2 & SHA3
Heterogeneous Computation for 2D Cylinder Flow Simulation
ECAS is a library for edge AI computing acceleration.
A set of microbenchmarks for deep copy in directive-based programming models
Learning how to design heterogeneous compute applications using OpenCL with an emphasis on GPU acceleration
Course Programming on new Architecture-1 (GPU), autumn 2021
Gecko - A programming model for heterogeneous environments
MagmaDNN Benchmarksuite for heterogenous architectures
AlexLens is a comprehensive Image Classification and Transfer Learning application, specifically designed for heterogeneous computing platforms. It features a custom-built AlexNet Neural Network for in-depth analysis and learning.
My MEng Computer Science thesis on the performance portability of the SYCL programming model
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