Benchmarking Parallel Electrocardiogram Compression Based on Successive Differences

Abstract

We focus on parallelization methods for an electrocardiogram data compression algorithm based on successive differences to gain insights into the advantages and disadvantages of parallel implementations. The experimental methodology exposes a comprehensive and systematic benchmarking process with varying input file sizes, hosting machine characteristics, and two popular parallelization approaches: OpenMP and MPI. We check the research hypothesis to see if parallelizing the compression algorithm can reduce the runtime while keeping the original algorithm’s compression results. Our analysis and discussion show that OpenMP outperforms MPI. An OpenMP implementation with 12 threads on a processor with six cores achieves the highest average speedup of 7 versus a single-thread implementation. Performance gains depend heavily on the utilized hardware and the degree of parallelism.