Compiler 2.0: Building the Next Generation of Compilers with Machine Learning
Saman Amarasinghe (MIT) presents on the integration of machine learning techniques into compiler design, exploring novel approaches for creating the next generation of high-performance compilers.
Biography:
Saman Amarasinghe is the Thomas and Gerd Perkins Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Commit Compiler Research Group. Under his leadership, the Commit group has developed a wide range of innovative programming languages and compilers, including StreamIt, StreamJIT, PetaBricks, Halide, TACO, Finch, SySTeC, GraphIt, Simit, MILK, Cimple, BioStream, NetBlocks, BREeze, CoLa, Shim, AskIt, and Seq. Additionally, the group has created compiler and runtime frameworks such as DynamoRIO, Helium, Tiramisu, Codon, BuildIt, and D2X, as well as vectorization techniques and tools such as Superword Level Parallelism (SLP), goSLP, and VeGen. Saman’s team also developed Ithemal, a machine-learning-based performance predictor; Program Shepherding, which protects programs from external attacks; the extensible OpenTuner autotuning framework; and Kendo, a deterministic execution system. He co-led the Raw architecture project. Outside academia, Saman has founded or co-founded several companies, including Determina, Lanka Internet Services Ltd., Venti Technologies, DataCebo, Exaloop, and Build4Sim. He earned his BS in Electrical Engineering and Computer Science from Cornell University in 1988, and his MSEE and Ph.D. from Stanford University in 1990 and 1997, respectively. He is a Fellow of the ACM and a recipient of the ACM-IEEE CS Ken Kennedy Award.
Abstract: