Current generations of NUMA node clusters feature multicore or manycore processors. Programming such architectures efficiently is a challenge because numerous hardware characteristics have to be taken into account, especially the memory hierarchy. One appealing idea to improve the performance of parallel applications is to decrease their communication costs by matching the communication pattern to the underlying hardware architecture. In this paper, we detail the algorithm and techniques proposed to achieve such a result: first, we gather both the communication pattern information and the hardware details. Then we compute a relevant reordering of the various process ranks of the application. Finally, those new ranks are used to reduce the communication costs of the application.

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Process Placement in Multicore Clusters:Algorithmic Issues and Practical Techniques

Modern hardware architectures featuring multicores and a complex memory hierarchy raise challenges that need to be addressed by parallel applications programmers. It is therefore tempting to adapt an application communication pattern to the characteristics of the underlying hardware. The MPI standard features several functions that allow the ranks of MPI processes to be reordered according to a graph attached to a newly created communicator. In this paper, we explain how theMPICH2 implementation of the MPI Dist graph create function was modified to reorder the MPI process ranks to create a match between the application communication pattern and the hardware topology. The experimental results on a multicore cluster show that improvements can be achieved as long as the application communication pattern is expressed by a relevant metric.

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Improving MPI applications performance on multicore clusters with rank reordering

The large number of processors in high performance computing and distributed applications is becoming a major challenge in the analysis of the way an application’s processes communicate with each other. In this paper, we propose an approach that facilitates the understanding of large traces of inter-process communication by extracting communication patterns that characterize their main behavior. Two algorithms are proposed. The first one permits the recognition of repeating patterns in traces of MPI (Message Passing Interface) applications whereas the second algorithm searches if a given communication pattern occurs in a trace. Both algorithms are based on the n-gram extraction technique used in natural language processing. Unlike existing work, our approach operates on the trace as it is generated (i.e. on the fly) and does not require complex and computationally-expensive data structures. We show the effectiveness and efficiency of our approach in detecting communication patterns from large traces generated from two target systems.

Pattern Recognition Techniques Applied to the Abstraction of Traces of Inter-Process Communication

Modern supercomputers are shared among thousands of users running a variety of applications. Knowing which applications are running in the system can bring substantial benefits: knowledge of applications that intensively use shared resources can aid scheduling; unwanted applications such as cryptocurrency mining or password cracking can be blocked; system architects can make design decisions based on system usage. However, identifying applications on supercomputers is challenging because applications are executed using esoteric scripts along with binaries that are compiled and named by users.

Taxonomist: Application Detection Through Rich Monitoring Data

Due to the advent of modern hardware architectures of high-performance comput- ers, the way the parallel applications are laid out is of paramount importance for performance. This chapter surveys several techniques and algorithms that efficiently address this issue: the mapping of the application's virtual topology (for instance its communication pattern) onto the physical topology. Using such strategy enables to improve the application overall execution time significantly. The chapter concludes by listing a series of open issues and problems.

https://hal.inria.fr/hal-00921626/document

An Overview of Process Mapping Techniques and Algorithms in High-Performance Computing

Interprocessor communication is an important factor in determining the performance scalability of parallel systems. The communication requirements of a parallel application can be quantified to understand its communication pattern and communication pattern similarities among applications can be determined. This is essential for the efficient mapping of applications on parallel systems and leads to better interprocessor communication implementation among others. This paper proposes a methodology to compare the communication pattern of distributed-memory programs. Communication correlation coefficient quantifies the degree of similarity between two applications based on the communication metrics selected to characterize the applications. To capture the network topology requirements, we extract the communication graph of each applications and quantities this similarity. We apply this methodology to four applications in the NAS parallel benchmark suite and evaluate the communication patterns by studying the effects of varying problem size and the number of logical processes (LPs).

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Ioana Romelia Pop

Papers

An approach for matching communication patterns in parallel applications