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Hardware-Software Co-Design to Improve Modern Workload Efficiency

Nishil Talati


Today’s explosive data growth has ushered a new generation of applications that  transform massive, unstructured, heterogeneous data into actionable knowledge. Data is  increasing exponentially in volume, velocity, variety, and complexity. On the other hand,  the performance of memory systems used to store and access this data has remained  almost constant throughout the years. Therefore, traditional memory systems cannot  keep up with the growing demands and complexities of data-intensive applications. 

In this talk, I will present my group’s research effort in optimizing the memory system  performance of a variety of data-intensive applications. In particular, I will present  Prodigy [HPCA 2021 Best Paper] in detail that uses a hardware-software co-designed  solution to improve the memory system performance of data-indirect irregular workloads in detail. Prodigy proposes a compact, yet efficient representation of program semantics  that communicates key workload information from software to hardware. Using compiler  analysis and hardware prefetching, Prodigy improves the end-to-end performance of  irregular workloads by more than 2.5x on CPUs. At the end, I will briefly summarize our other recent/ongoing works on optimizing other interesting data-intensive workloads.



Nishil Talati is an Assistant Research Scientist (Research Faculty) at the CSE department of University of Michigan. He earned his PhD from University of Michigan. Nishil’s  research interests include computer architecture and systems software design for  improving the performance of modern data-intensive workloads. His research is published  at several top-tier venues including ISCA, MICRO, HPCA, ASPLOS, and others. Nishil’s work has been recognized as the 2021 HPCA best paper award, 2023 DATE and 2023 IISWC best paper honorable mentions. 

Nishil Talati Headshot
Nishil Talati
University of Michigan
ECE 125
9 Jan 2024, 10:30am until 11:30am