2013 Poster Sessions : Composition and Reuse with Compiled Domain-Specific Languages

Student Name : Arvind Sujeeth
Advisor : Oyekunle Olukotun
Research Areas: Computer Systems
Abstract:
Programmers who need high performance currently rely on low-level, architecture-specific programming models (e.g. OpenMP for CMPs, CUDA for GPUs, MPI for clusters). Performance optimization with these languages usually requires expertise in the specific programming model and a deep understanding of the target architecture. Domain-specific languages (DSLs) are a promising alternative, allowing compilers to map application-specific abstractions directly to low-level architecture-specific programming models. However, developing these DSLs is difficult, and using multiple DSLs together in a single application is even harder because existing compiled solutions do not compose together. In this poster, we present four new performance-oriented DSLs developed with Delite, an extensible DSL compilation framework. We highlight new techniques to compose compiled DSLs and show that generic optimizations can be applied across different DSLs in the same program. Our new DSLs are implemented with a small number of reusable components (less than 9 parallel operators total) and still achieve performance up to 125x better than library implementations and at worst within 30% of optimized stand-alone DSLs. The DSLs retain good performance when composed together, and applying cross-DSL optimizations results in up to an additional 1.82x improvement.

Bio:
Arvind Sujeeth is a Ph.D. candidate working for Prof. Kunle Olukotun. He is interested in leveraging domain-specific languages as a parallel programming platform. His research focuses on building reusable infrastructure for embedded parallel DSLs.