Prof. Saman P Amarasinghe

Professor of Computer Science and Engineering
Faculty Director, Global Startup Labs

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 32-G744

Areas of Interest and Expertise

Compiler Optimizations
Computer Architecture
Software Engineering and Parallel Computing
Program Analysis and Optimization
Oxygen Project
Big Data

Research Summary

Professor Amarasinghe works to find novel approaches to improve the performance of computer systems from the angle of programming languages and compilers. Programming languages tell computers what to do in a precise way, and compilers take the high-level descriptions of programs and map them in a way that can be run on the computers. Compilers bring programs from high-level programming language to a simple machine language. One area of research Amarasinghe is looking into is new programming languages in different computation domains (from image processing to quantum chromodynamics) and maximizing performance in specific areas in these domains. Domain-specific, high-performance compilers will help researchers in various domains get the performance they need to focus on research experiments, instead of spending a majority of their time writing and optimizing code.

Projects led by Amarasinghe include domain-specific languages Halide and Simit. Halide is specific to image processing, and addresses the challenge of getting high performance out of image processing pipelines that compose multiple stencil computations, complex reductions, and global or data-dependent access patterns as stages connected in a complex stream program. Halide is becoming the industry standard language for image processing and is heavily adopted by Google, Adobe, Facebook, and Qualcomm. Simit is a language that makes it easy to compute on sparse systems using linear algebra. Simit programs are often simpler and shorter than equivalent MATLAB programs, yet are comparable in performance to hand-optimized codes.

Professor Amarasinghe is also working with industry on projects such as the Tensor Algebra Compiler (Taco) for dense and sparse linear and tensor algebra expressions, and is investigating the use of synthetic data to improve the privacy of data and data security for organizations.

Recent Work