Zdraveski, Vladimir
Preferred name
Zdraveski, Vladimir
Official Name
Zdraveski, Vladimir
Main Affiliation
Email
vladimir.zdraveski@finki.ukim.mk
56 results
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Item type:Publication, Parallelism in Signature Based Virus Scanning with CUDA(Springer International Publishing, 2019) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bi-Source Verification Against Silent Data Corruption in High Performance Computing(ACM, 2019-09-26) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic intelligent load balancing in power distribution networks(Elsevier BV, 2015-12); ; Kocarev, Ljupco - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parallel Near-Duplicate Document Detection Using General-Purpose GPU(Central Library of the Slovak Academy of Sciences, 2024); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic Load Balancing and Reactive Power Compensation Switch Embedded in Power Meters(Institute of Electrical and Electronics Engineers (IEEE), 2017-04); ; ; Kocarev, Ljupco - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parallel Implementation of an Algorithm for Atrial Fibrillation Detection Using RR Intervals and Shannon Entropy(IEEE, 2018-11) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Programmable vs. Fixed-Function Pipeline in Real-Time Computer Graphics(IEEE, 2019-07); - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Benchmarking Parallel Electrocardiogram Compression Based on Successive Differences(IEEE, 2024-11-26); Gusev, MarjanWe 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.