This paper evaluates the suitability of the MapReduce
model for multi-core and multi-processor systems. MapRe-
duce was created by Google for application development
on data-centers with thousands of servers. It allows pro-
grammers to write functional-style code that is automati-
cally parallelized and scheduled in a distributed system.
We describe Phoenix, an implementation of MapReduce
for shared-memory systems that includes a programming
API and an efficient runtime system. The Phoenix run-
time automatically manages thread creation, dynamic task
scheduling, data partitioning, and fault tolerance across
processor nodes. We study Phoenix with multi-core and
symmetric multiprocessor systems and evaluate its perfor-
mance potential and error recovery features. We also com-
pare MapReduce code to code written in lower-level APIs
such as P-threads. Overall, we establish that, given a care-
ful implementation, MapReduce is a promising model for
scalable performance on shared-memory systems with sim-
ple parallel code. | 