Mon 20 - Fri 24 October 2014 Portland, Oregon, United States
Wed 22 Oct 2014 13:30 - 13:52 at Salon E - Domain Specific Languages Chair(s): Yannis Smaragdakis

Graphics processing units (GPUs) can effectively accelerate many applications, but their applicability has been largely limited to problems whose solutions can be expressed neatly in terms of linear algebra. Indeed, most GPU programming languages limit the user to simple data structures–typically only multidimensional rectangular arrays of scalar values. Many algorithms are more naturally expressed using higher level language features, such as algebraic data types (ADTs) and first class procedures, yet building these structures in a manner suitable for a GPU remains a challenge. We present a region-based memory management approach that enables rich data structures in Harlan, a language for data parallel computing. Regions enable rich data structures by providing a uniform representation for pointers on both the CPU and GPU and by providing a means of transferring entire data structures between CPU and GPU memory. We demonstrate Harlan’s increased expressiveness on several example programs and show that Harlan performs well on more traditional data-parallel problems.

Wed 22 Oct

13:30 - 15:00: OOPSLA - Domain Specific Languages at Salon E
Chair(s): Yannis SmaragdakisUniversity of Athens
oopsla2014141397740000013:30 - 13:52
Eric HolkIndiana University, Ryan R. NewtonIndiana University, Jeremy G. Siek, Andrew LumsdaineIndiana University
Link to publication
oopsla2014141397875000013:52 - 14:15
Richard UhlerMIT-CSAIL, Nirav DaveSRI International
Link to publication
oopsla2014141398010000014:15 - 14:37
Jeffrey BosboomMIT CSAIL, Sumanaruban RajaduraiNational University of Singapore, Weng-Fai WongNational University of Singapore, Saman AmarasingheMIT
Link to publication
oopsla2014141398145000014:37 - 15:00
Emma ToschUniversity of Massachusetts, Amherst, Emery BergerUniversity of Massachusetts, Amherst
Link to publication File Attached