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Software . 2026
License: CC BY
Data sources: Datacite
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
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GTPin: Enhancing Intel® GPU Profiling With High-Level Binary Instrumentation

Authors: Levit-Gurevich, Konstantin; Skaletsky, Alex; Geller, Ortal; Goren, Moshe; Ziadat, Rawan; Burstein, Rami; Oved, Tal;

GTPin: Enhancing Intel® GPU Profiling With High-Level Binary Instrumentation

Abstract

This artifact contains the code used to reproduce the results presented in the paper "GTPin: Enhancing Intel GPU Profiling With High-Level Binary Instrumentation" accepted to 2026 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS '26).

Keywords

GTPin, Profiling, GPU, Intel, Dynamic Binary Instrumentation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average