L
Larry Rudolph
Researcher at Massachusetts Institute of Technology
Publications - 117
Citations - 7563
Larry Rudolph is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Scheduling (computing) & Shared memory. The author has an hindex of 39, co-authored 117 publications receiving 7328 citations. Previous affiliations of Larry Rudolph include Carnegie Mellon University & Hebrew University of Jerusalem.
Papers
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Journal ArticleDOI
Competitive snoopy caching
TL;DR: This work presents new on-line algorithms to be used by the caches of snoopy cache multiprocessor systems to decide which blocks to retain and which to drop in order to minimize communication over the bus.
Book ChapterDOI
Theory and Practice in Parallel Job Scheduling
TL;DR: The scheduling of jobs on parallel supercomputer is becoming the subject of much research, however, there is concern about the divergence of theory and practice, and a proposal for standard interfaces among the components of a scheduling system is proposed.
Journal ArticleDOI
Dynamic Partitioning of Shared Cache Memory
TL;DR: The results show that smart cache management and scheduling is essential to achieve high performance with shared cache memory and can improve the total IPC significantly over the standard least recently used (LRU) replacement policy.
Proceedings ArticleDOI
A new memory monitoring scheme for memory-aware scheduling and partitioning
TL;DR: A scheme that enables an accurate estimate of the isolated miss-rates of each process as a function of cache size under the standard LRU replacement policy is described, which can be used to schedule jobs or to partition the cache to minimize the overall miss-rate.
Journal ArticleDOI
Gang scheduling performance benefits for fine-grain synchronization
Dror G. Feitelson,Larry Rudolph +1 more
TL;DR: A model to evaluate the performance of different combinations of synchronization mechanisms and scheduling policies leads to the conclusion that gang scheduling is required for efficient fine-grain synchronization on multiprogrammed multiprocessors.