Timezone: »
Computer systems have become increasingly complicated through increased system specialization and heterogeneity designed to meet an increasingly diverse set of system requirements across scale, performance, energy efficiency, reliability, and quality of results. With automated system optimization opportunities being driven by predictive models of system behavior, traditional strategies for manually developing predictive behavioral models have become increasingly more complicated and less precise with growing system complexity.
In this talk, I'll present DiffTune, a technique for learning neurosymbolic performance models of modern computer processors. Processor performance models are critical for many computer systems engineering tasks, however, due to the limits on our ability to introspect modern processors, these models must be inferred from behavioral measurements. Our system leverages deep learning to perform differentiable surrogate optimization of a CPU simulator to yield models that predict the performance of programs executed on modern Intel CPUs better than state-of-the-art, handcrafted techniques from LLVM.
Our approach demonstrates that behavioral models can be effectively learned from data as well as can be constructed to provide an interpretation of their predictions through behavioral traces grounded in the execution of a simulator.
Author Information
Michael Carbin (MIT)
More from the Same Authors
-
2022 Poster: Pruning’s Effect on Generalization Through the Lens of Training and Regularization »
Tian Jin · Michael Carbin · Dan Roy · Jonathan Frankle · Gintare Karolina Dziugaite -
2020 Poster: The Lottery Ticket Hypothesis for Pre-trained BERT Networks »
Tianlong Chen · Jonathan Frankle · Shiyu Chang · Sijia Liu · Yang Zhang · Zhangyang Wang · Michael Carbin -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 Poster: Compiler Auto-Vectorization with Imitation Learning »
Charith Mendis · Cambridge Yang · Yewen Pu · Saman Amarasinghe · Michael Carbin