12,054 research outputs found
Task mapping on a dragonfly supercomputer
The dragonfly network topology has recently gained traction in the design of high performance computing (HPC) systems and has been implemented in large-scale supercomputers. The impact of task mapping, i.e., placement of MPI ranks onto compute cores, on the communication performance of applications on dragonfly networks has not been comprehensively investigated on real large-scale systems. This paper demonstrates that task mapping affects the communication overhead significantly in dragonflies and the magnitude of this effect is sensitive to the application, job size, and the OpenMP settings. Among the three task mapping algorithms we study (in-order, random, and recursive coordinate bisection), selecting a suitable task mapper reduces application communication time by up to 47%
Idle Period Propagation in Message-Passing Applications
Idle periods on different processes of Message Passing applications are
unavoidable. While the origin of idle periods on a single process is well
understood as the effect of system and architectural random delays, yet it is
unclear how these idle periods propagate from one process to another. It is
important to understand idle period propagation in Message Passing applications
as it allows application developers to design communication patterns avoiding
idle period propagation and the consequent performance degradation in their
applications. To understand idle period propagation, we introduce a methodology
to trace idle periods when a process is waiting for data from a remote delayed
process in MPI applications. We apply this technique in an MPI application that
solves the heat equation to study idle period propagation on three different
systems. We confirm that idle periods move between processes in the form of
waves and that there are different stages in idle period propagation. Our
methodology enables us to identify a self-synchronization phenomenon that
occurs on two systems where some processes run slower than the other processes.Comment: 18th International Conference on High Performance Computing and
Communications, IEEE, 201
Scientific Computing Meets Big Data Technology: An Astronomy Use Case
Scientific analyses commonly compose multiple single-process programs into a
dataflow. An end-to-end dataflow of single-process programs is known as a
many-task application. Typically, tools from the HPC software stack are used to
parallelize these analyses. In this work, we investigate an alternate approach
that uses Apache Spark -- a modern big data platform -- to parallelize
many-task applications. We present Kira, a flexible and distributed astronomy
image processing toolkit using Apache Spark. We then use the Kira toolkit to
implement a Source Extractor application for astronomy images, called Kira SE.
With Kira SE as the use case, we study the programming flexibility, dataflow
richness, scheduling capacity and performance of Apache Spark running on the
EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an
equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon
EC2 cloud. Furthermore, we show that by leveraging software originally designed
for big data infrastructure, Kira SE achieves competitive performance to the C
implementation running on the NERSC Edison supercomputer. Our experience with
Kira indicates that emerging Big Data platforms such as Apache Spark are a
performant alternative for many-task scientific applications
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