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The impact of vectorization and parallelization of the slope algorithm on performance and energy efficiency on multi-core architecture

Bylina Beata, Potiopa Joanna, Klisowski Michał, Bylina Jarosław

Annals of Computer Science and Information Systems

2021

Vol. 25

283--290

Calculation of land-surface parameters (e.g. slope, aspect, curvature) is an important part of many geospatial analyses. Current research trends are aimed at developing new software techniques to achieve the best performance and energy trade-off. In our work, we concentrate on the vectorization and parallelization to improve overall energy efficiency and performance of the neighborhood raster algorithms for the computation of land-surface parameters. We chose the slope calculation algorithm as the basis for our investigation. The parallelization was achieved through redesigning the the original sequential code with OpenMP SIMD vectorization hints for compiler, OpenMP loop parallelization, and the hybrid of these techniques. To evaluate both performance and energy savings, we tested our vector-parallel implementations on a multi-core computer for various data sizes. RAPL interface was used to measure energy consumption. The results showed that optimization towards high performance can also be an effective strategy for improving energy efficiency.

A method for testing the performance of a dual-core microcontroller with integrated program and data memory

Sieczkowski K., Sondej T.

Measurement Automation Monitoring

2015

Vol. 61, No. 7

320--322

The paper presents a set of test procedures for performing an extended test of dual-core microcontroller performance. The procedures described enable the performance of the system to be determined with regard to memory organization, mutual influence of cores and ability to verify the impact of code optimization level. The experimental trials were performed using an LPC4357 microcontroller with onboard FLASH and SRAM memory. The results obtained showed a slight performance drop for the various microcontrollers operating simultaneously in typical conditions. However, with frequent references to a shared memory, the performance drop may reach as much as 80%.