End of Parlab talk. May 29, 2013 Gans Srinivasa Intel Corp

Transcription

1 End of Parlab talk May 29, 2013 Gans Srinivasa Intel Corp 1

2 A Journey: Year 2005; 2008; 2011; Potential Parallel Speedup MIMD*SIMD (32 b) MIMD*SIMD (64 b) SIMD (32b) SIMD (64b) MIMD 10 To Multicore 4 to 6 cores To Double digits Potomac 8M 90nm 675M T 322M T 1 1.3B T 354 mm mm mm Paxville 2x2M 90nm Tulsa 16M 65nm Merom Tigerton NEW Microarchitecture 65nm (Merom) 65nm Penryn NEW NEW Process 45nm (Penryn) Microarchitecture 45nm Dunnington Nehalem 45nm Westmere NHM-EX NEW 45nm (NHMCore) Process 32nm Sandy Bridge Sandy bridge NEW Microarchitecture 32nm Jaketown) ~70% higher The Mantra: Parallelize and Customize. Otherwise Peril! Market is driving the Energy Efficiency and TCO 2 Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. Other names and brands may be claimed as the property of others. All products, dates, and figures are preliminary and are subject to change without any notice.

3 UPCRC Phase-II Correctness UPCRC Phase-I Enable most programmers to be productive writing efficient, correct, portable SW for 100+ cores & scale as cores increase every 2 years (!). Let apps drive this. Parallel Applications Initial meetings Parallel Hardware Common Kernals? Building blocks Prog Models Connecting them How to describe apps and kernals? How to program the HW Evaluation Berkeley View Tech Report Computational motifs common across apps Power, Memory, ILP walls forces industry to bet on parallel micro processors-multicore. Real world naturally parallel, HW gotten parallel. Idea: Computational motifs common across applications High-level pattern constrains space of reasonable low-level mappings App 1 App 2 App 3 Motifs = Dwarfs Dense Sparse Graph Trav. Win UPCRC Competition Par Lab focus Mobile clients 2007 Compelling Applications drive the Research Agenda Parlab Tessellation OS: Applications Composed of Interconnected Cells Communication Avoidance Algorithms Refine Towards Implementation A = M x V Easy to write correct software that runs efficiently on manycorespeech Personal Health Image Retrieval Hearing, Music Parallel Browser Design Pattern Language (OPL) Composition & Coordination Language (C&CL) High level, C&CL Compiler/Interpreter safe, concurrency through high level frameworks Parallel Libraries Parallel Frameworks Static Verification Type Systems 2010 Efficiency Languages Legacy Code Schedulers Sketching Autotuners Communication & Synch. Primitives Directed Testing Dynamic Checking 2011 Speaker Diarization Legacy OS Intel Multicore/GPGPU Efficiency Language Compilers Readable Layouts OS Libraries & Services Hypervisor RAMP Manycore Debugging with Replay 2012 Integrated DEMOS Great SUCESS Music Recommendation Original goal: Desktop-quality browsing on handhelds (Enabled by 4G networks, better output devices) Now: Better development environment for new mobile-client applications, merging characteristics of browsers and frameworks (Silverlight, Qt, Android) 2013 Par Lab End New user interfaces with pressuresensitive multi-touch gestural interfaces 120-channel speaker array Video Event Detection 2014 Programmable virtual instrument and audio processing 3 Country Fair 2011 Par Lab focus Mobile clients AMPLab focuses on Cloud Both together ( Client+Cloud ) ParLab-AMPLab collaborations

4 Motifs for parallel algorithms Berkeley View 13 dwarfs 1. Dense Linear Algebra 2. Sparse Linear Algebra 3. Spectral Methods 4. N-Body Methods 5. Structured Grids 6. Unstructured Grids 7. MapReduce 8. Combinational Logic 9. Graph Traversal 10. Dynamic Programming 11. Backtrack and Branch-and-Bound 12. Graphical Models 13. Finite State Machines OPL/PLPP 2012 The Berkeley View (Asanovic et al. 2006) 4

5 Retreat information Indicators Boot Camps Total Faculty Students Industry Staff % Industry Participation January 9-11, June 4-6, January 7-9, June 1-3, January 13-15, May 24-26, January 12-14, June 1-3, January 4-6, May 30 - June 1, Courses Year wise Course taught on parallelism related / Registrants Online courses/ Coverage 2008 CS267: Applications of parallel computers (Demmel, Yelick) CS294: Architecting Parallel applications (Keutzer, Mattson 2009 CS267: Applications of parallel computers (Demmel, Yelick) CS294: Designing Parallel Software with Design Patterns (Keutzer, Mattson 2010 CS267: Applications of parallel computers (Demmel, Yelick) CS294: Architecting parallel software using patterns (Keutzer, Mattson) CS194: Architecting parallel Software (Keutzer, Murphy, Mattson) 2011 CS267: Applications of parallel computers (Demmel, Yelick) 2012 CS267: Applications of parallel computers (Demmel, Yelick) CS194 (spring): Architecting Parallel applications (Keutzer, Mattson) CS194 (fall): Architecting Parallel Applications (Keutzer, Mattson) Year Total Industry Acadamia Didn t Specity Online - worldwide % Industry Participation On site CS267 and CS194 online courses under development with funding from the NSF Papers, Reports Year Total Papers Tech Reports Graduated Year Graduated Exceeds Expectations

6 How does Parlab influence the Future? Computing for US : My Health Where we want to be? 6

7 Fighting Cancer in Future Patient arrives at oncologist with entire sequence of his cancer s genome Mutations organized into key pathways Software identifies key pathways contributing to growth of cancer Therapies target these pathways after tumor removed Patients starts with 1 st drug cocktail, switch to 2 nd when cancer mutates, switch to 3 rd when mutates again Take some medicine for rest of life? Emperor of All Maladies, page ? Or Shall we challenge this for 2020

8 All under a few days? 2020 Challenge 1-4 days More than a week + Accuracy Issues Could Take Months. Still in early stages of Research Patient arrives at the clinic Genome sequencing Computational needs: MAP it Machines, Algorithms & Processing Systems Biomedicine and Development of Personalized Treatments Construct the Genome DNA/RNA Pipeline Pathway networks to predict various drug responses (Graph models, Bayesian models & ML) Cell response & methylation data Development of Mechanistic Understanding Identify treatment options: & Integrate Analysis Identify various factors and decide the patient s treatment options. Models for Drug Treatment and Response 6/12/ DNA/RNA Pipelines Towards Personalized Medicine

9 My Health : Application Space Biological sequence analysis Massively parallel sequencing Cross-genomics Variation detection Network biology and integration Regulation of mrna and mirna expression Network reconstruction Systems modeling Image processing and registration Image transforms and enhancement Feature generation Classification and registration Dense Linear A Sparse Linear A Spectral Methods N- Body Struct Grids Unstruct Grids Map Reduce Comb Logic Graph Traversal Dyna Prog B track & Branch Graphical Models FSM Kemal Sonmez OHSU; Gans- Intel 9

10 Parlab: You exceed expectations You inspire the future. We need all the help to meet the 2020 Challenge! Thank You. 10

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