savannah , ga 09 march 2010 john a. turner, ornl allen malony , univ. of oregon

Slide 1

SOS14 Panel Discussion

Software programming environments and tools for GPUsWhere are they today? What do we need for tomorrow?Savannah, GA09 March 2010John A. Turner, ORNLAllen Malony, Univ. of OregonSimone Melchionna, EPFL Mike Heroux, SNL#Cray Confidential Cray Proprietary1 U. S. Department Of Energy 2

DOE INCITE applications span a broad range of science challengesProjects2006200720082009Accelerator physics1111Astrophysics3455Chemistry1124Climate change3345Combustion1122Computer science1111Fluid Dynamics11Fusion4535Geosciences111High energy physics11Life sciences2224Materials science 2334Nuclear physics 2212Industry2333Total Projects:22283038CPU Hours:36,156,00075,495,000145,387,000469,683,000#Application ChallengesNode-level Concurrency finding sufficient concurrency to amortize or hide data movement coststhread creation and managementData movementmanaging memory hierarchyminimizing data movement between CPU and GPUProgramming modelsexpressing the concurrency and data movement mentioned aboverefactoring applications undergoing active development maintaining code portability and performanceCompilers and toolsavailability, quality, usabilityability to use particular language features (e.g. C++ templates)#3How are you programming GPUs today?In preparing apps for hybrid / accelerated / many-core platforms, what is your preferred path for balancing performance, portability, and future-proofing?What is the typical effort required to refactor applications for hybrid / accelerated / many-core platforms?What role does performance modeling play, if any?What role can / should it play?Software programming environments and tools for GPUs(Where are they today? What do we need for tomorrow?)#4Software programming environments and tools for GPUs(Where are they today? What do we need for tomorrow?)What programming environment tools are key to preparing applications for coming platforms?What tools are most in need of improvement & investment?Do the application modifications for existing and near-term platforms (Roadrunner, GPU-accelerated systems) better prepare them for other future platforms envisioned in the Exascale roadmap?#5What is difficult or missing from your current programming models? What important architectural features must be captured in new programming models? What are the possible future programming models 10 and 20 years from now?Software programming environments and tools for GPUs(Where are they today? What do we need for tomorrow?)#6Software programming environments and tools for GPUs(Where are they today? What do we need for tomorrow?)In previous major shifts such as vector to distributed-memory parallel, some applications did not survive or required such major re-factoring that for all intents and purposes they were new codes.What fraction of applications would you predict will fail to make this transition?Will the survival rate be significantly different between science apps and engineering apps?Software has become more complex since the last major transition, but also tends to make greater use of abstraction. In the end does this help or hinder applications in moving to these architectures?Is there an aspect to survival that could be viewed as a positive rather than a negative? That is, does the end of the road for an application really present an opportunity to re-think the physics / math / algorithms / implementations?

#7Allen Malony slides#8Simone MelchionnaEPFLHigh Performance Simulations of Complex Biological Flowson CPUs and GPUsEPFL, CHHarvard, USCNR, Italy#9Multi-scale Hemodynamics

(0, length, op);DotOp op;op.x = ...; op.y = ...;float dot;dot = node.parallel_reduce< DotOp > (0, length, op);Work with Chris Baker, ORNL#16Some Additional ThoughtsNew Programming Models/Environments:Must compete/cooperate with MPI.MPI advantage: Most code is serial.New PM/E:Truly useful if it solve the ubiquitous markup problem.Conjecture: Data-intensive apps will drive new PM/Es.Our community will adopt as opportunities arise.Source of new PM/Es: Industry.Best compilers are commercial.Runtime system is critical.#17

savannah , ga 09 march 2010 john a. turner, ornl allen malony , univ. of oregon

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