Aumage, Olivier

Beltran, Vicenç

Brown, Nicholas

Ciesko, Jan

Lörwald, Bernd

Rahn, Mirko

Rotaru, Tiberiu

Sistek, Jakub

Teruel, Xavier

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      A directory/cache for leveraging the efficient use of distributedmemory by task-based runtime systemsCitation for published version:Rotaru, T, Lörwald, B, Brown, N, Rahn, M, Aumage, O, Beltran, V, Teruel, X, Ciesko, J & Sistek, J 2018, 'Adirectory/cache for leveraging the efficient use of distributed memory by task-based runtime systems' 5thInternational Conference on Exascale Applications and Software, Edinburgh, United Kingdom, 17/04/18 -19/04/18, .Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 14. Jun. 2018EASC 2018A directory/cache for leveraging the efficient use of distributedmemory by task-based runtime systemsTiberiu Rotaru, Bernd Lo¨rwald, Nick Brown, Mirko Rahn, Olivier Aumage, Vicenc Beltran,Xavier Teruel, Jan Ciesko, Jakub Sˇ´ıstekAs the community progresses towards exascale the appearance of much more complex archi-tectures than we are currently used to leveraging is likely. For instance, machines which arehighly parallel, with a large number of multi-core nodes, deep memory hierarchies and complexinterconnect topologies are around the corner. Efficiently programming these new systems willbe a huge challenge, where programmers will not only need to fundamentally rethink their codeto increase the level of parallelism by at least an order of magnitude, but to also address otherissues such as resilience.Task-based models [1] are one possible solution to this challenge, where parallelism is de-composed into many tasks. By rethinking their parallelism in the paradigm of tasks, onecan significantly reduce synchronisation which is key for achieving high levels of concurrency.The underlying task model decouples the management of parallelism from computation. Thisrelieves the application developers from dealing with lower level details such as scheduling,memory management and resilience concerns that are tricky to manage in large computingsystems.However task-based models are not a silver bullet and this paradigm often focuses aroundproviding the abstraction of a single shared address space to the application programmer. Toscale beyond a single physical memory space then some sort of distribute technology (e.g. MPIor GASPI) must be combined. This interoperability is either at the task based runtime level(and implicit to the programmer) or involves explicit communications calls provided by theprogrammer within tasks of their application code.We have developed an API for a Directory/Cache [2] which can be integrated with task-based runtimes and seamlessly (to the applications programmer) provides the abstraction ofa single shared address space. Supporting interoperability between task-based models anddistributed memory technologies, whilst memory is physically distributed amongst the nodesthe Directory/Cache enables this to be presented to the applications programmer as a single,unified memory space. The directory tracks what data is physically stored where and the cacheis used for performance to avoid frequently retrieving the same piece of remote data. The mainpurpose of the Directory/Cache is to provide a set of services that support task-based runtimesystems efficiently running distributed applications, while being able to consistently managedata stored in distributed memory or in local caches.We have developed a reference implementation of our Directory/Cache API which, to illustratethe abstract and generic nature of our API, has been or is being integrated with the runtimesof the OmpSs, StarPU, GPI-Space and PaRSEC task-based models. The Directory/CacheAPI allows runtimes to be completely independent from the physical representation of dataand from the type of storage used. This facilitates access through the same interface to anextendable list of transport implementations using different communication libraries such asGASPI and MPI and even on-disk storage or tiered memory.In this talk we will describe the main concepts behind our API and the underlying architectureof the reference implementation. We will also present the results of integrating the Direc-tory/Cache with popular task-based models and specifically the performance and scalabilitythat this affords to real-world applications utilising this technology.References[1] Ferna´ndez, A., et al. ”Task-based programming with OmpSs and its application.” European Conference onParallel Processing. Springer International Publishing, 2014.[2] Hedo, J. ”Run-time support for multi-level disjoint memory address spaces.” PhD thesis, 2015

A directory/cache for leveraging the efficient use of distributed memory by task-based runtime systems

https://www.pure.ed.ac.uk/ws/files/58837251/EASC2018_paper_23_2_.pdf

A directory/cache for leveraging the efficient use of distributed memory by task-based runtime systems

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer