Charith Mendis
- Assistant Professor
- Department of Computer Science
- University of Illinois at Urbana-Champaign
- Office:
- 4118 Siebel Center for Computer Science
- 201 N. Goodwin Ave., Urbana IL 61801
- Email : charithm '@' illinois 'dot' edu
- Github : CharithYMendis
- Twitter : @charith_mendis
- LinkedIn : CharithMendis

Our group has multiple PhD positions available for Fall 2023. If you are interested in doing novel program optimization and compiler research targeting domains such as ML and data science, please be in touch with me and consider applying for PhD positions at UIUC CS.
I am an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.
Before joining UIUC, I spent a year at Google Brain working as a Visiting Faculty Researcher. Prior to that, I completed my PhD at Massachusetts Institute of Technology (MIT) advised by Prof. Saman Amarasinghe. Earlier, I completed my masters at MIT and my bachelors at University of Moratuwa. I was awarded the William A. Martin Memorial Thesis Award for my masters thesis at MIT and the institute Gold Medal for my bachelors.
Research Interests
My broad research interests include programming languages, compilers and machine learning. More specifically, I am interested in using data-driven approaches to solve hard systems optimization problems (ML for Systems), for example, in compilers, and to develop high performance systems that can aid in compute intensive machine learning tasks (Systems for ML).
I envision a future where most compiler optimizations will be auto-generated as opposed to manually written. Not only will they produce faster code, but also they will be easier to develop and maintain. I belive both machine learning and formal methods have a role to play in realizing this vision. In the ML front, we have used data-driven techniques to develop novel compiler cost models (Ithemal, TPU cost model) and learn end-to-end compiler optimization policies (Vemal) that outperform hand-crafted tools. Using formal reasoning, we have automatically generated correct-by-construction compiler auto-vectorizers (Vegen). I believe the synergy between both machine learning and formal methods will help compilers evolve organically to ever changing workloads and hardware architectures.
In my earlier work, I have also developed techniques to rejuvenate performance of already hand-optimized codes which are susceptible to bit-rot using both dynamic analysis (Helium) as well as static analysis (Revec) techniques.
Students
PhD Students:
- Stefanos Baziotis (Fall 2021 - )
- Damitha Lenodara (Fall 2021 - )
- Ahan Gupta (Fall 2022 - )
- Avaljot Singh (Fall 2022 - ) - Co-advised with Gagandeep Singh
- Yishen Chen - Co-advised with Saman Amarasinghe at MIT
MS and Undergraduate Students:
- Yufeng Wang (Fall 2021 - )
- Tianfan Xu (Senior)
Teaching
- CS 598CM: ML for Compilers and Architecture (Fall 2021, Fall 2022)
- CS 526: Advanced Compiler Construction (Spring 2022)
- CS 591ACT: Advanced Compiler Technology Seminar (Spring 2022)
Service
- PC : PLDI 2023, MLSys 2023, PACT 2022, PLDI 2022, CGO 2022, PACT 2021, SPLASH SRC 2021
- ERC : ASPLOS 2023, ASPLOS 2022, ASPLOS 2021, PACT 2020
- Reviewer : ICML 2022, NeurIPS 2021, ICLR 2021 (Outstanding Reviewer), ICML 2021
Publications
Conference Publications
-
SPADE: A Flexible and Scalable Accelerator for SpMM and SDDMM
Gerasimos Gerogiannis, Serif Yesil, Damitha Lenadora, Dingyuan Cao, Charith Mendis, Josep Torrellas
ISCA 2023 (Accepted)
-
Unified Convolution Framework: A compiler-based approach to support sparse convolutions
Jaeyeon Won, Changwan Hong, Charith Mendis, Joel Emer, Saman Amarasinghe
MLSys 2023 (Accepted)
-
TGOpt: Redundancy-Aware Optimizations For Temporal Graph Attention Networks
Yufeng Wang, Charith Mendis
PPoPP 2023
[PDF] [Code] [Bibtex]
-
WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program
Jaeyeon Won, Charith Mendis, Joel Emer, Saman Amarasinghe
ASPLOS 2023
[PDF] [Bibtex]
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GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Ondrej Sykora, Amir Yazdanbakhsh, Phitchaya Phothilimthana, Charith Mendis
IISWC 2022
[PDF] [Bibtex]
-
All you need is Superword-Level Parallelism: Systematic Control-Flow Vectorization with SLP
Yishen Chen, Charith Mendis, Saman Amarasinghe
PLDI 2022
[PDF] [Bibtex]
-
Vegen: A Vectorizer Generator for SIMD and Beyond
Yishen Chen, Charith Mendis, Michael Carbin, Saman Amarasinghe
ASPLOS 2021
[PDF] [Bibtex]
-
A Learned Performance Model for Tensor Processing Units
Samuel Kaufman, Phitchaya Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, Mike Burrows
MLSys 2021
[ArXiv]
-
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
MICRO 2020
[ArXiv] [Bibtex]
-
Compiler Auto-Vectorization with Imitation Learning
Charith Mendis, Cambridge Yang, Yewen Pu, Saman Amarasinghe, Michael Carbin
NeurIPS 2019
[PDF] [Bibtex]
-
BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models
Yishen Chen, Ajay Brahmakshatriya, Charith Mendis, Alex Renda, Eric Atkinson, Ondrej Sykora, Saman Amarasinghe, Michael Carbin
IISWC 2019
[PDF] [Project Page] [Bibtex]
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Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Saman Amarasinghe, Michael Carbin
ICML 2019
[PDF] [Project Page] [Bibtex]
Best Paper Award (ML for Systems workshop @ISCA 2019)
Press - MIT News, Slashdot, i-programmer, Register UK -
Revec: Program Rejuvenation through Revectorization
Charith Mendis*, Ajay Jain*, Paras Jain, Saman Amarasinghe
CC 2019
[PDF] [Project Page] [Bibtex]
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goSLP: Globally Optimized Superword Level Parallelism Framework
Charith Mendis, Saman Amarasinghe
PACMPL(OOPSLA) 2018
[PDF] [Project Page] [Bibtex]
-
Making Caches Work for Graph Analytics
Yunming Zhang, Vladimir Kiriansky, Charith Mendis, Saman Amarasinghe, Matei Zaharia
IEEE BigData 2017
[PDF] [Bibtex]
Best Student Paper Award
-
Parallelizing WFST Speech Decoders
Charith Mendis, Jasha Droppo, Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz, Geoffrey Zweig
ICASSP 2016
[PDF]
-
Helium: Lifting High-Performance Stencil Kernels from Stripped x86 Binaries to Halide DSL Code
Charith Mendis, Jeffrey Bosboom, Kevin Wu, Shoaib Kamil, Jonathan Ragan-Kelley, Sylvain Paris, Qin Zhao, Saman Amarasinghe
PLDI 2015
[PDF] [Project Page] [Bibtex]
Press - Fortune News, MIT News, Adobe blog, Yahoo Tech, Computer Business Review
Theses
-
Towards Automated Construction of Compiler Optimizations
Charith Mendis
PhD Thesis, Massachusetts Institute of Technology
[PDF] [Bibtex]
-
Helium: Lifting High-Performance Stencil Kernels from Stripped x86 Binaries to Halide DSL Code
Charith Mendis
SM Thesis, Massachusetts Institute of Technology
[PDF] [Bibtex]
William A. Martin Memorial Thesis Prize
Selected Awards
- Best Paper Award - ML for Systems workshop @ISCA 2019 (link)
- Best Student Paper Award - IEEE BigData 2017
- William A. Martin Memorial Thesis Prize for the best SM thesis in Computer Science at MIT (link)
- MIT Energy Initiative Fellowship 2013-2014 (link)
- Sri Lanka Telecom Gold Medal for the best student at University of Moratuwa majoring Electronics and Telecommunication Engineering