D4.2 Profiling report of the partner’s tools, complete with performance suggestions

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D4.2 Profiling report of the partner’s tools, complete with performance suggestions

R. Amela, R. Badia, S. Böhm, R. Tosi

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Contact: rtosi@cimne.upc.edu
International Centre for Numerical Methods in Engineering

, C. Soriano

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Contact: csoriano@cimne.upc.edu
International Centre for Numerical Methods in Engineering

, R. Rossi

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Contact: rrossi@cimne.upc.edu
International Centre for Numerical Methods in Engineering

Abstract

This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were

dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the

objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel.

A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels,

for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally,

while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to

be scheduled and stress the scheduling engines.

A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model

and dynamic scheduler of the tasks involved in the executions.

While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace

generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs.

The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity

tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded

engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than

the one that will be used for the real scenarios.

The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.

How to cite

R. Amela, R. Badia, S. Böhm, R. Tosi, C. Soriano and R. Rossi, D4.2 Profiling report of the partner’s tools, complete with performance suggestions, Open Access Repository of the ExaQUte project: Deliverables (2021). 4 URL https://www.scipedia.com/public/table_Soriano_2019b

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