Pauli, Armin ; Buelow, Max von ; Ströter, Daniel (2024)
In-Situ Profiling Feedback for GPGPU Code.
doi: 10.26083/tuprints-00027344
Report, Erstveröffentlichung, Preprint
Kurzbeschreibung (Abstract)
The evolving landscape of software development increasingly prioritizes functionality, maintainability, and developer productivity. This typically comes hand in hand with the shortcoming that less focus is invested on optimizing for runtime performance of programs. However, optimizing for performance is an important task in time-critical domains. Additionally, optimizing for performance can be an important way of reducing actual hardware requirements and achieving a better ecological footprint. So, why not bringing program optimization closer to the software engineer and reducing the disconnect between profiling results and their interpretability? This poster presents a GPU-focused in-situ profiling approach that visualizes memory profiling metrics directly inside the source code and gives the software engineer an direct hint for identifying inefficient parts during development. Performance metrics evaluated on each line are highlighted in the source code.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Pauli, Armin ; Buelow, Max von ; Ströter, Daniel |
| Art des Eintrags: | Erstveröffentlichung |
| Titel: | In-Situ Profiling Feedback for GPGPU Code |
| Sprache: | Englisch |
| Publikationsjahr: | 10 Mai 2024 |
| Ort: | Darmstadt |
| Kollation: | 2 ungezählte Seiten |
| DOI: | 10.26083/tuprints-00027344 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27344 |
| Kurzbeschreibung (Abstract): | The evolving landscape of software development increasingly prioritizes functionality, maintainability, and developer productivity. This typically comes hand in hand with the shortcoming that less focus is invested on optimizing for runtime performance of programs. However, optimizing for performance is an important task in time-critical domains. Additionally, optimizing for performance can be an important way of reducing actual hardware requirements and achieving a better ecological footprint. So, why not bringing program optimization closer to the software engineer and reducing the disconnect between profiling results and their interpretability? This poster presents a GPU-focused in-situ profiling approach that visualizes memory profiling metrics directly inside the source code and gives the software engineer an direct hint for identifying inefficient parts during development. Performance metrics evaluated on each line are highlighted in the source code. |
| Status: | Preprint |
| URN: | urn:nbn:de:tuda-tuprints-273445 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 10 Mai 2024 12:51 |
| Letzte Änderung: | 13 Mai 2024 06:03 |
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| Suche nach Titel in: | TUfind oder in Google |
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