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Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review

Suejb Memeti, Sabri Pllana, Alecio Binotto, Joanna Kolodziej, and Ivona Brandic

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Tags: Computer science, Machine learning, Metaheuristics, parallel computing, software optimization

February 10, 2018 by suejb.memeti

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP-AMD: Extending CUDA to AMD Platforms

Kernel Tuner

Testing and Mutation Testing for GPU Kernels

GT4Py: GridTools for Python

GT4Py: High Performance Stencils for Weather and Climate Applications using Python

miniBUDE: A BUDE virtual-screening benchmark, in many programming models

Evaluation of FPGA-based high performance computing platforms

gossip: Efficient Communication Primitives for Multi-GPU Systems

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

AUCMEDI: a framework for Automated Classification of Medical Images

Frameworks in Medical Image Analysis with Deep Neural Networks

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

An approach to performance portability through generic programming

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