Geveler, Markus

Turek, Stefan

Eldorado - Ressourcen aus und für Lehre, Studium und Forschung

Fundamentals of a numerical cloud computing for applied sciences –Preparing cloud computing for `Simulation-as-a-Service´Markus Geveler*, Stefan Turek°* markus.geveler@math.tu-dortmund.de° ture@featflow.deInstitute for Applied MathematicsTU DortmundAbstractThis paper is supposed to be used as a contribution for the `Consultation on Cloud Computing ResearchInnovation Challenges for WP 2018-2020´ as called for by the European Commission (DG CONNECT, unit`Cloud and software´).  We propose to encourage and support  fundamental  interdisciplinary research formaking the benefits generated by cloud computing accessible to the applied science community. Introduction: Why cloud computing and high performance computing are contradictingThe basic idea of cloud computing (CC) is to abstract from an IT infrastructure including compute-,memory-,  networking-  and  software  resources  by  virtualization.  These  resources  are  madeaccessible to the user in a dynamic and adaptive way. The major resulting advantages comparedto  a  specially  tailored  `in-house  solution´  can  be  found  in  a  transparent  and  simple  usage,enhanced flexibility due to scalability and adaptivity to a specific need and finally in the increasedefficiency  due  to  savings  in  energy  and  money  spent.  The  latter  is  due  to  scaling  effects,operational efficiency, consolidation of resources and reduction of risks. The application is literallyindependent  from  any  (local)  data  and  compute  resources  as  these  can  be  concentratedeffectively. All together, these advantages may some day supersede the traditional local / regionaldata center approach which can be found on the level of modern universities and research centers.From  the  point  of  view  of  data  center  management  and  operations,  CC  leads  to  a  higheroccupancy and therefore efficiency: The inevitable granularity effects that occur with medium orlarge workloads can be tackled with a backfilling of many small jobs. In addition, due to the factthat a specific application run's need for resources may vary from time to time, left-over capacitiescan be provided in a profitable `pay per use´ style.In High Performance Computing (HPC) on the other hand, virtualization and abstraction conceptscontradict  the usual  approaches especially in  the simulation  of  technical  processes:  Here,  thefocus is put on enhancing the performance of an application by explicitly optimize for a certain typeof hardware. This requires an a priori knowledge of the hardware which usually is given by the fact,that universities and regional research facilities have their own local or regional compute centerswith comparatively static hardware components.  This point of view can in some cases generateseveral  orders of  magnitude of  performance gains and we call  this  concept  hardware-orientednumerics. This paradigm comprises the simultaneous optimization for hardware, numerical andenergy efficiency on all levels of application development [1,2,3,4]. One effort in hardware-orientednumerics  is  to  optimize  code  and  develop  or  choose  numerical  methods  with  respect  to  aheterogeneous  hardware  ecosystem:  Multicore  CPUs  are  as  straight-forward  as  hardwareaccelerators like GPUs, FPGAs, Xeon Phi  processors and system on a chip designs such asARM-based CPUs with integrated GPUs. In addition, there are non-uniform memory architectureson the device level as well as heterogeneous communication infrastructures on the cluster level.The usual design pattern however is to optimize code for a (single) given hardware configurationwhere the simulation code is then optimized in a comparatively expensive way due to this proximityto hardware details. This development process is therefore the complete opposite of relying on avirtualization approach.Today's scientific cloud computing is not feasible for numerical simulationUp  to  today  all  efforts  to  make  use  of  CC  techniques  in  the  science  community  can  becharacterized by what we call  scientific cloud computing (SCC), which basically has been verysuccessful for a specific type of application: In the scope of Big Data often a direct projection of aproblem  to  a  bag  of  tasks  programming  model  can  be  found.  Also  other  problems  that  areconstituted by smaller  independent  tasks,  where the coupling  and therefore  communication  isminimal or zero can be coped with easily in a cloud environment. In numerical simulation on theother hand a strong coupling of  the very computationally intense subproblems is  the standardcase.  This  induces  a  comparatively  high  synchronization  need,  requiring  low  communicationlatencies. The execution models of CC are literally blind for this type of strong coupling becausethe virtualization shuts down any attempt to optimize inter process communication.  We believe,that the development of numerical simulation software should be characterized by the synthesis ofhardware, numerical, and energy efficiency. Hence for this type of application a CC concept whichtakes into account the heterogeneity of compute hardware would be most feasible: According toour vision in future scenarios the user of such codes might want to choose for run time optimizationin different metrics: Flexibility in the selection in which way a specific run should be allocated to acertain type(s) of compute node(s) are required. This flexibility has not been accounted for in thedevelopment of numerical code frameworks yet. A direct result of the service providers internalizingthe concept of hardware-oriented numerics would be that the user of the service would be able tomake an a priori choice for the core requirements for the run. For instance it could be decidedwhether an allocation of hardware should be made in order to minimize wall clock time or minimizeenergy to solution. Other hardware specifics could be made allocateable such as the type andproperties of the communication links between nodes. The service would then return a number ofallocations based upon available hardware. After selection, a complex optimization problem thenhas  to  be  solved:  The  simulation  software  has  to  be  able  to  select  numerical  algorithmiccomponents that fit  to this allocation and finally, a load balancing has to be performed for theindividual problem to be solved.Towards a numerical cloud computingIn order to realize this vision, there are two fundamental problems to solve:(1) Specially tailored numerics as well as load balancing strategies as well as (2) mapping, scheduling and operation strategies for numerical simulation have to be developed.In (1) numerical components in a code framework have to be revisited or developed from scratchwith respect  to (2)  by adjusting them to the respective strategies.  Such numerical  alternativesrange from preconditioners in linear solvers to whole discretization approaches on the model level.Different hardware specific implementations have to be provided and tuned in order to enable theoptimizer in (2) to succeed, which is closely related to performance engineering. This has to beundergone with respect to all levels of parallelism in modern hardware architectures and on alllevels of an application.On the other hand, the systems / strategies developed in (2) have to be sensitive for theeffects of specific numerics on specific hardware. This problem is often closely related to numericalscaling, convergence and complexity theories. These theories and related skills are usually notaddressed as an integral part of the training in computer science or service providers / operators.Here an automatic tuning system has to be developed that is capable of deciding what type ofnumerics is to be used for a given hardware allocation and which parts of the data are distributedto which part of the hardware by a static or even dynamic load balancing. The latter is an evenmore complex  problem keeping in  mind the heterogeneity  even within  one specific  allocation,where CPUs are for instance to be saturated alongside GPUs. This optimization problem is verysimilar to how compilers schedule instructions on the processor level. It is also multi-dimensional,as not only raw performance has to be optimized for but also energy to solution as stated in theprevious section.Hence  we  emphasize  that  these  two  components,  (1)  and  (2),  cannot  be  brought  upindependently: Specialists from the domain of applied mathematics, performance engineers andapplication  specialists  are  required  for  the  former,  whereas  the  latter  is  to  be  coped  with  bycomputer sciences and service providers / specialists.ConclusionFor the reasons brought up in this consulting paper, we propose to extend the flexibility of scientificcloud  computing  towards  a  numerical  cloud  computing  in  the  following  way:  Given  aheterogeneous cloud hardware architecture on the node level, with a priori unknown specifics andamount of nodes in an allocation, software frameworks for numerical simulation have to be enabledto `react´ on the properties of this allocation as described in the previous section. This would makepossible the continuation of hardware-oriented numerics and thus combine the advantages of avery hardware-centric approach with those of virtualization.For the sake of such a `Simulation-as-a-Service´ interdisciplinary research is needed that fusesknowledge in classical cloud computing approaches with experiences from the applied sciences.AcknowledgementsThis work has been supported in part by the German Research Foundation (DFG) through thePriority Program 1648 ‘Software for Exascale Computing’ (grant TU 102/48) as well as grant TU102/50-1.References[1] GEVELER, M., RIBBROCK, D., GÖDDEKE, D., ZAJAC, P., AND TUREK, S. 2013. Towards a complete FEM–based simulation toolkit on GPUs: Unstructured grid finite element geometric multigrid solvers with strong smoothers based on sparse approximate inverses. Computers and Fluids 80 (July), 327–332. doi: 10.1016/j.compfluid.2012.01.025.[2] GEVELER, M., REUTER , B., AIZINGER, V., GÖDDEKE, D., AND TUREK, S. 2016. Energy efficiency of the simulation of three-dimensional coastal ocean circulation on modern commodity and mobile processors –a case study based on the Haswell and Cortex-a15 microarchitectures. Computer Science – Research and Development, 1-10 (June). doi: 10.1007/s00450-016-0324-5.[3]  TUREK, S., BECKER , C., AND K ILIAN , S. 2006. Hardware–oriented numerics and concepts for PDE software. Future Generation Computer Systems 22, 1–2, 217–238. doi:10.1016/j.future.2003.09.007.[4] TUREK, S., GÖDDEKE, D., BECKER , C., BUIJSSEN, S., AND WOBKER, H. 2010. FEAST —- realisation of hardware–oriented numerics for HPC simulations with finite elements. Concurrency and Computation: Practice and Experience 6 (May), 2247–2265. Special Issue Proceedings of ISC 2008. doi:10.1002/cpe.1584.

Fundamentals of a numerical cloud computing for applied sciences

https://eldorado.tu-dortmund.de/bitstream/2003/35801/1/Ergebnisbericht%20Nr.%20555.pdf

Fundamentals of a numerical cloud computing for applied sciences

Abstract

Similar works

Full text

Available Versions

Eldorado - Ressourcen aus und für Lehre, Studium und Forschung