38 research outputs found
Sorting Integers on the AP1000
Sorting is one of the classic problems of computer science. Whilst well
understood on sequential machines, the diversity of architectures amongst
parallel systems means that algorithms do not perform uniformly on all
platforms. This document describes the implementation of a radix based
algorithm for sorting positive integers on a Fujitsu AP1000 Supercomputer,
which was constructed as an entry in the Joint Symposium on Parallel Processing
(JSPP) 1994 Parallel Software Contest (PSC94). Brief consideration is also
given to a full radix sort conducted in parallel across the machine.Comment: 1994 Project Report, 23 page
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load