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This paper proposes to automate much of this effort using dynamic instrumentation to inform dynamic, profile-driven optimizations.
It measures an application's runtime behavior and selectively applies optimizations during the execu- tion of the application. Leo achieves this by integrating.
This paper proposes to automate much of this effort using dynamic instrumentation to inform dynamic, profile-driven optimizations. In this vision, the ...
Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications. N. Farooqui, C. Rossbach, Y. Yu, and K. Schwan. TRIOS, USENIX Association, (2014 ).
Dec 22, 2021 · TRIOS '14 - Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications. USENIX · 35:20 · TRIOS '14 - Proactive Energy-Aware ...
Jul 24, 2024 · Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications. TRIOS 2014. [c3]. view. electronic edition via DOI; unpaywalled ...
Leo: A {Profile-Driven} Dynamic Optimization Framework for {GPU} Applications. N Farooqui, CJ Rossbach, Y Yu, K Schwan. 2014 Conference on Timely Results in ...
Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications Naila Farooqui, Christopher J. Rossbach, Yuan Yu, Karsten Schwan 2014 Conference ...
Leo: a profile-driven dynamic optimization framework for GPU applications · Author Picture Naila Farooqui. Georgia Institute of Technology. , · Author Picture ...
Leo: a profile-driven dynamic optimization framework for GPU applications · Naila Farooqui,Christopher J. Rossbach,Yuan Yu,Karsten Schwan +3 moreGeorgia ...