45th Meeting

  • Speaker: Angelina Lee (MIT)
  • Date: May 9th, 2013 (Thursday)
  • Room: CIT 368
  • Title: Memory Abstractions for Parallel Programming
  • Abstract:

A memory abstraction is an abstraction layer between the program execution and the memory that provides a different “view” of a memory location depending on the execution context in which the memory access is made. Properly designed memory abstractions help ease the task of parallel programming by mitigating the complexity of synchronization and/or admitting more efficient use of resources. In this talk, I will demonstrate this point using two case studies on two types of memory abstractions.

The first memory abstraction is the cactus stack memory abstraction in Cilk-M, a Cilk-based work stealing runtime system. Many multithreaded concurrency platforms that use a work-stealing runtime system incorporate a “cactus stack” to support multiple stack views for all the active children simultaneously. The use of cactus stacks, albeit essential, forces concurrency platforms to trade off between performance, memory consumption, and interoperability with serial code due to its incompatibility with linear stacks. We proposes a new strategy to build a cactus stack using thread-local memory mapping, which allows worker threads to have their respective linear views of the cactus stack. This cactus stack memory abstraction enables a concurrency platform that employs a work-stealing runtime system to satisfy all three criteria
simultaneously.

The second memory abstraction is reducer hyperobjects (or reducers for short), a linguistic mechanism that helps avoid determinacy races in dynamic multithreaded programs. The Cilk-M runtime system supports reducers using the memory-mapping approach, which utilizes thread-local memory mapping and leverages the virtual-address translation provided by the underlying hardware to implement this memory abstraction. This memory mapping approach yields a close to 4x faster access time compared to the existing approach of implementing reducers.

I-Ting Angelina Lee is a postdoctoral associate in Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, working with Prof. Charles E. Leiserson. Her primary research interest is in the design and implementation of programming models, languages, and runtime systems to support multithreaded software. She received her Ph.D. from MIT, under the supervision of Prof. Charles E. Leiserson. In her Ph.D. thesis, she investigated several “memory abstractions,” which help ease the task of parallel programming. Her prior work includes the “ownership-aware” transactional-memory methodology, the first transactional memory design that provides a structured programming style with provable safety guarantees for “open-nested” transactions, and JCilk, a variant of Java with multithreading provided by Cilk’s fork-join primitives, that has exception-handling semantics which integrate synergistically with those primitives. She received her Bachelor of Science in Computer Science from UC San Diego, where she worked on the Simultaneous Multithreading Simulator for DEC Alpha under the supervision of Prof. Dean Tullsen.

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