It is critical to performance engineering that we can investigate the efficiency of applications in utilizing hardware components. As new hardware technologies, including hardware accelerators and fast, non-volatile memory technologies emerge and be integrated into systems, it is essential that profiling tools must allow performance engineers to re-examine the interactions between applications and emerging hardware technologies.
Extreme Storage & Computer Architecture Laboratory (ESCAL) at UC Riverside led by Prof. Hung-Wei Tseng recently released two profiling tools — TPUPoint (https://github.com/escalab/
Tier Memory Profiler (TMP) is a tool released jointly by researchers from ESCAL (Jinyoung Choi and Hung-Wei Tseng) and AMD (Sergey Blagodurov). The IPDPS paper written by the authors of TMP also reveals that through TMP’s more complete understanding about the memory usage of big-data applications, the same memory allocation policy can work more efficiently. In other words, the profiling tool matters and probably even more than policies!
For more information regarding to Prof. Hung-Wei Tseng and his group’s research, please visit ESCAL’s website at http://www.escalab.org/
Please also take a look of our GitHub repositories for
TPUPoint — https://github.com/escalab/
TMP — https://github.com/escalab/
 Abenezer Wudenhe and Hung-Wei Tseng, "TPUPoint: Automatic Characterization of Hardware-Accelerated Machine-Learning Behavior for Cloud Computing," 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2021, pp. 254-264. https://ieeexplore.ieee.org/
 Jinyoung Choi, Sergey Blagodurov and Hung-Wei Tseng. Dancing in the Dark: Profiling in the Age of Tiered Memory. In 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2021.