Memory-mapping support for reducer hyperobjects

I-Ting Angelina Lee, Aamir Shafi, Charles E. Leiserson
2012 Proceedinbgs of the 24th ACM symposium on Parallelism in algorithms and architectures - SPAA '12  
Reducer hyperobjects (reducers) provide a linguistic abstraction for dynamic multithreading that allows different branches of a parallel program to maintain coordinated local views of the same nonlocal variable. In this paper, we investigate how thread-local memory mapping (TLMM) can be used to improve the performance of reducers. Existing concurrency platforms that support reducer hyperobjects, such as Intel Cilk Plus and Cilk++, take a hypermap approach in which a hash table is used to map
more » ... ucer objects to their local views. The overhead of the hash table is costly -roughly 12× overhead compared to a normal L1-cache memory access on an AMD Opteron 8354. We replaced the Intel Cilk Plus runtime system with our own Cilk-M runtime system which uses TLMM to implement a reducer mechanism that supports a reducer lookup using only two memory accesses and a predictable branch, which is roughly a 3× overhead compared to an ordinary L1-cache memory access. An empirical evaluation shows that the Cilk-M memory-mapping approach is close to 4× faster than the Cilk Plus hypermap approach. Furthermore, the memory-mapping approach admits better locality than the hypermap approach during parallel execution, which allows an application using reducers to scale better.
doi:10.1145/2312005.2312056 dblp:conf/spaa/LeeSL12 fatcat:ewpy3jsrczbqdb76gfgilh5jvu