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Record W4404129034 · doi:10.24018/compute.2024.4.5.135

A Comparative Study of CPU and GPU Power Consumption while using Open-Source and Proprietary Media Players

2024· article· en· W4404129034 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Information Technologies and Computer Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Remote Desktop Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPower consumptionOpen sourceComputer scienceCPU shieldingConsumption (sociology)Power (physics)Operating systemCentral processing unitParallel computingSoftwareSociology

Abstract

fetched live from OpenAlex

This study presents a comparative analysis of power consumption between open-source and proprietary media players when playing open-media format videos (.webm). As media consumption grows, energy-efficient software is critical for both environmental sustainability and device performance. Using tools like HWiNFO, key metrics such as GPU and CPU power consumption, memory usage, and efficiency were evaluated for popular open-source (e.g., VLC, Kodi) and proprietary (e.g., GOM Player, KMPlayer) players. The results reveal that open-source players generally consume less GPU power but more CPU resources, while proprietary players balance CPU and GPU usage with higher memory demands. The findings suggest that careful selection of media players can lead to significant energy savings over time, offering insights for developers and users focused on energy-efficient computing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.263
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it