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11	Design and evaluation of a plain MPI-based cluster execution backend for the SkePU 3 skeleton programming framework Zeijlon, Alexander January 2023 (has links) SkePU 3 is a framework for parallel program execution that uses higher order functions called skeletons, which provide a layer of abstraction between user code and the parallel implementation it provides through its backends. The backend that enables SkePU to run on an HPC cluster has a slowdown of a factor two. This reduces the viability of SkePU as an alternative for HPC, and as such, warrants an investigation. Programs written in SkePU are sequential-looking, single-source C++ programs where skeleton calls can transparently execute on multiple different types of processing units, such as CPU cores, GPUs and clusters, using different backends. In this thesis, a strategy for improving the performance of SkePU on clusters is presented, and with it, the design and implementation of a new cluster backend that is simpler and more closely integrated with the non-cluster SkePU code base. Runtime measurements are made, which show that the new cluster backend sees a relative speedup of about a factor of two, which effectively eliminates the slowdown. SkePU skeleton programming algorithmic skeletons HPC cluster parallel programming MPI OpenMP CUDA Hybrid NUMA Computer Sciences Datavetenskap (datalogi)
12	Integrating SkePU's algorithmic skeletons with GPI on a cluster / Integrering av SkePUs algoritmiska skelett med GPI på ett cluster Almqvist, Joel January 2022 (has links) As processors' clock-speed flattened out in the early 2000s, multi-core processors became more prevalent and so did parallel programming. However this programming paradigm introduces additional complexities, and to combat this, the SkePU framework was created. SkePU does this by offering a single-threaded interface which executes the user's code in parallel in accordance to a chosen computational pattern. Furthermore it allows the user themselves to decide which parallel backend should perform the execution, be it OpenMP, CUDA or OpenCL. This modular approach of SkePU thus allows for different hardware to be used without changing the code, and it currently supports CPUs, GPUs and clusters. This thesis presents a new so-called SkePU-backend made for clusters, using the communication library GPI. It demonstrates that the new backend is able to scale better and handle workload imbalances better than the existing SkePU-cluster-backend. This is achieved despite it performing worse at low node amounts, indicating that it requires less scaling overhead. Its weaknesses are also analyzed, partially from a design point of view, and clear solutions are presented, combined with a discussion as to why they arose in the first place. SlePU cluster GPI MPI OpenMP parallel programming HPC algorithmic skeletons distributed computing SkePU kluster GPI MPI OpenMP parallellprogrammering HPC algoritmiska skelett Computer Sciences Datavetenskap (datalogi)

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Design and evaluation of a plain MPI-based cluster execution backend for the SkePU 3 skeleton programming framework

Integrating SkePU's algorithmic skeletons with GPI on a cluster / Integrering av SkePUs algoritmiska skelett med GPI på ett cluster