OpenMP scalability limits on large SMPs and how to extend them

  • OpenMP-Skalierbarkeitslimits auf großen SMP-Systemen and wie sie erweitert werden können

Schmidl, Dirk; Müller, Matthias S. (Thesis advisor); Bischof, Christian (Thesis advisor)

Aachen (2016)
Dissertation / PhD Thesis

Dissertation, RWTH Aachen University, 2016


The most widely used node type in high-performance computing nowadays is a 2-socket server node. These nodes are coupled to clusters with thousands of nodes via a fast interconnect, e.g. Infiniband. To program these clusters the Message Passing Interface (MPI) became the de-facto standard. However, MPI requires a very explicit expression of data layout and data transfer in a parallel program which often requires the rewriting of an application to parallelize it. An alternative to MPI is OpenMP, which allows to incrementally parallelize a serial application by adding pragmas to compute-intensive regions of the code.This is often more feasibly than rewriting the application with MPI. The disadvantage of OpenMP is that it requires a shared memory and thus cannot be used between nodes of a cluster. However, different hardware vendors offer large machines with a shared memory between all cores of the system.However, maintaining coherency between memory and all cores of the system is a challenging task and so these machines have different characteristics compared to the standard 2-socket servers. These characteristics must be taken into account by a programmer to achieve good performance on such a system. In this work, I will investigate different large shared memory machines to highlight these characteristics and I will show how these characteristics can be handled in OpenMP programs. When OpenMP is not able to handle different problems, I will present solutions in user space, which could be added to OpenMP for a better support of large systems. Furthermore, I will present a tools-guided workflow to optimize applications for such machines.I will investigate the ability of performance tools to highlight performance issues and I will present improvements for such tools to handle OpenMP tasks. These improvements allow to investigate the efficiency of task-parallel execution, especially for large shared memory machines.The workflow also contains a performance model to find out how well the performance of an application is on a system and when to stop tuning the application.Finally, I will present two application case studies where user codes have been optimized to reach a good performance by applying the optimization techniques presented in this thesis.