GPGPU Programming Courses: Getting the Word Out to the

Test and Evaluation Community

ITEA Technical Review:Cyber Security

21 July 2010Gene Wagenbreth for Robert F. Lucas, D.M. Davis and K-T. Yao { genew, rflucas, ddavis, kyao }@isi.edu

Outline

Why Might T&E Professionals use GPGPU Technology

ISI’s Experience in Using ItTeaching Courses to Various

GroupsLessons Learned Analyzing Needs and Capabilities

GPGPUs and T&E

T&E can effectively use more computing that it isAn approach to reducing costs, time and physical

danger, is to adopt heterogeneous computing. Heterogeneous computing aids the CPUs, using:

General Purpose Graphics Processing Units (GPGPUs)Field Programmable Gate Arrays (FPGAs)Digital Signals Processors (DSPs)Sony/ Toshiba/IBM (STI) Cell processors

There is a growing body of evidence on the benefits of using these devices

This talk will concentrate on GPGPUs

Trends in Computational Power

GPU Architecture

Multiple SIMD cores

MultithreadedO(1000) per

GPU

Banked shared memory

32 Kbytes Tesla48 Kbytes

Fermi

Simple thread modelOnly sync at

host

Courtesy NVIDIA

Why GPUs?

●GPU performance can be 100X hosts• Don’t forget Prof. Gene Amdahl, 2-3X

typical• This differential is expected to grow

●Early Simulation work (UNC & SAIC)• Line of Sight• Route Finding• Collision Detection• Sparse Matrix Factorization (see RFLucas

paper)

●ISI verified they’re also bottlenecks in SAFs

●New ideas for use in sensor scenario creation for new multi-spectral sensors

CUDA TrainingPET Courses

●Dr. David Pratt conceived and organized • HPCMP FAPOC for FMS

●Location & Dates: • SAIC facility Suffolk VA, 23 - 25 October

2007• ISI Marina del Rey 21- 23 October 2008• UCSD San Diego 5 – 6 March 2009

●Attendees: total ~ 60 HPCMP users●Also taught at USC as part of Parallel

Programming Class

NVIDIA Instructors

Paulius MicikeviciousPatrick Legresley

Course Materials are On-Line

Course was very well received by participantsAll completed the courseLast session devolved into a very spirited discussion of

possible usesMaterials are on-lineURL for .pdf presentations slides:http://www.isi.edu/~ddavis/GPU/Course/Slides/GPU+CUDA.pdf80% participants completed surveyRankings all quite high

Views of Class Participants

West Coast Class in Preparation for this Fall

Success of class in Suffolk encouragingHigh attendance Good reviews Introduced HPC to many new programmersLots of requests to repeat

Considered West Coast eventMany HPC users in San Diego, L.A., Monterey, Bay areaNext event at SPAWAR San Diego or ISI Marina del ReyProbably in late September early October, 2 – 3 daysContact Dan Davis ddavis@isi.edu 310 448-8434 or

watch URL http://www.isi.edu/~ddavis/GPGPU/Course2

Practicum and Programming Sessions

Gene Wagenbreth gives programming

exercises

Patrick Legresley helps with

implementation

Very Early Programming

Trained ISI staff with Sparse Matrix SolverAlso Looked at line-of-sight

Looks promisingRarely peaks in way GPGPUs necessaryPeaks do drive overall performanceSeeking optimum approach to final programming model

Initiating work on router planningEarly results encouragingMay have broad application

Lessons Learned

Three distinct ways of looking at teachingGeneral Classrooms SettingFocused Instruction

with LabsPracticum with

Follow-up Assistance

Pros and Cons to each

General Classrooms Setting

Didactic approachEasy to prepare for, like reading a

scriptGeneral coveragePros

Covers wide areas of interest to programmerFormal and organized

ConsMisses the markSpend time on issues that won’t be used

Focused Instruction with Labs

Lecture some; practice someGets hands on experience Pros

Keeps interest upSpot weaknesses of students

ConsUneven advancement of different

studentsNeeds more than one teacher

Practicum with Follow-up AssistanceLittle lecturing

Almost immediate hands onHelp-desk-like support laterPros

Focus on areas to be usedNot spend time covering material

understood

ConsMay not get good coverage of materialSome students may be lost

Finding the Right Approach

Each situation differentAnalyze what you are doingConsider your people carefullyPick approach you

Can supportFeel your people we acceptThink will meet your goals

Analyzing Needs and Capabilities

First look at your goalsCode productionMorale builderProfessional studies

Second, consider contraintsTimeFunding Production Schedule

The Instructor’s Characteristics

Useful qualificationsParallel programming experienceExcellent communicatorStrong egoComfortable with groups

Also instructor needsA couple of months to master CUDAAssistance in hands on tutoring

Different Student Types

Student shouldBe journeyman programmersKnow C, as CUDA has a C-like “feel”Have some dedicated time freeWant to learn CUDA

TypesEager learnersSelf taught CUDA “exerts”Foot dragging, sour pusses

Assessing the Impact

Evaluation sheet at end of courseCareful logging of requests for

help Follow up with supervisorsQuestionnaire after working with

CUDA for six monthsOn-line survey as to usage

Evangelizing and Building a Community

Training does more than just establish skill level in students

Acts to inform students and their communities as to benefits of GPGPU programming

Establish Birds of a Feather sessions at conferences

Encourage papers and web sites.

Resources and Classes

Slides from ISI Coursehttp://www.isi.edu/~ddavis/rfl/CS503-GPU.ppt

CUDA Downloadshttp://developer.nvidia.com/object/cuda_3_1_downloads.html

CUDA Manualhttp://developer.download.nvidia.com/compute/cuda/3_1/toolkit/docs/CudaReferenceManual.pdf

Conclusions

GPGPUs are an important potential benefit to T&ECUDA makes this use feasibleTraining will both inform and motivate adoptionTrainers need to be carefully selectedStudents easily pick up conceptsEvaluation later will identify those who are

inclined to this kind of effortLongevity of this technique will be sufficient to

justify training, beyond that is up the fate

This material is based on research sponsored by the U.S. Joint Forces Command via a contract with the Lockheed Martin Corporation and SimIS, Inc., and on research sponsored by the Air Force Research Laboratory under agreement numbers F30602-02-C-0213 and FA8750-05-2-0204. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government. Approved for public release; distribution is unlimited.

Research Funded by JFCOM and AFRL