Computer Science Thesis Proposal
In Person - Reddy Conference Room, Gates Hillman 4405
JATIN ARORA , Ph.D. Student, Computer Science Department, Carnegie Mellon University
Provably Efficient Coscheduling of Computation and Data through Disentanglement
Because of its many desirable properties, such as its ability to control effects and thus potentially disastrous race conditions, functional programming offers a viable approach to programming modern multicore computers. Over the past decade several parallel functional languages, typically based on dialects of ML and Haskell, have been developed. These languages, however, have traditionally underperformed compared to procedural languages (such as C and Java). The primary reason for this is their hunger for memory, which only grows with parallelism, causing traditional memory management techniques to buckle under increased demand for memory.
Building on a long line of work on parallel memory management, this thesis proposes provably and practically efficient techniques for memory management of parallel functional programs. The key idea behind our techniques is to coschedule the parallel computation with its data, enabling the memory manager to exploit our disentanglement hypothesis—the idea that parallel tasks of a program largely execute independently and avoid side-effecting data that may be accessed by others—for efficiency. We implement these techniques in the MPL compiler for parallel ML and our experimental results show that the techniques can marry the safety benefits of functional programming with performance.
Umut Acar (Chair)
Yongshan Ding (Yale University)
Rustan Leino (Amazon)