1. PRACE Keynote, Linz Oskar Mencer, April 2014 Computing in Space

3. Thinking Fast and Slow Daniel Kahneman Nobel Prize in Economics, 2002 14 × 27 = ? Kahneman splits thinking into: System 1: fast, hard to control System 2: slow, easier to control ….. 300 ….. 378

4. Assembly-line computing in action SYSTEM 1 x86 cores SYSTEM 2 flexible memory plus logic Low Latency Memory System High Throughput Memory minimize data movement Optimal Encoding Optimal Encoding

5. A program is a sequence of instructions Performance is dominated by: – Memory latency – ALU availability 5 Temporal Computing (1D) CPU Time Get Inst. 1 Memory COMPCOMP Read data 1 Write Result 1 COMPCOMP Read data 2 Write Result 2 COMPCOMP Read data 3 Write Result 3 Actual computation time Get Inst. 2 Get Inst. 3

6. 6 Spatial Computing (2D) data in data in ALU Buffer ALU Control ALU Control ALU data out data out Synchronous data movement Time Read data [1..N] Computation Write results [1..N] Throughput dominated

7. Computing in Time vs Computing in Space Computing in Time 512 Controlflow Cores 2GHz 10KB on-chip SRAM 8GB on board DRAM 1 result every 100* clock cycles *depending on application! Computing in Space 10,000* Dataflow cores 200MHz 5MB on-chip SRAM 96GB of DRAM per DFE 1 result every clock cycle => *200x faster per manycore card => *10x less power => *10x bigger problems per node => *10x less nodes needed >10TB/s

8. 8 New CME Electronic Trading Gateway will be going live in March 2014! Webinar Page: http://www.cmegroup.com/education/new-ilink-architecture-webinar.html CME Group Inc. (Chicago Mercantile Exchange) is one of the largest options and futures exchanges. It owns and operates large derivatives and futures exchanges in Chicago, and New York City, as well as online trading platforms. It also owns the Dow Jones stock and financial indexes, and CME Clearing Services, which provides settlement and clearing of exchange trades. …. [from Wikipedia] OpenSPL in Practice

9. 9

10. Maxeler Seismic Imaging Platform Maxeler provides Hardware plus application software for seismic modeling MaxSkins allow access to Ultrafast Modelling and RTM for research and development of RTM and Full Waveform Inversion (FWI) from MatLab, Python, R, C/C++ and Fortran. Bonus: MaxGenFD is a MaxCompiler plugin that allows the user to specify any 3D Finite Difference problem, including the PDE, coefficients, boundary conditions, etc, and automatically generate a fully parallelized implementation for a whole rack of Maxeler MPC nodes. Application areas: O&G Weather 3D PDE Solvers High Energy Physics Medical Imaging Application areas: O&G Weather 3D PDE Solvers High Energy Physics Medical Imaging 10

11. Example: data flow graph generated by MaxCompiler 4866 static dataflow cores in 1 chip

12. Mission Impossible?

13. Computing in Space - Why Now? 13 Semiconductor technology is ready – Within ten years (2003 to 2013) the number of transistors on a chip went up from 400M (Itanium 2) to 5Bln (Xeon Phi) Memory performance isn’t keeping up – Memory density has followed the trend set by Moore’s law – But Memory latency has increased from 10s to 100s of CPU clock cycles – As a result, On-die cache % of die area increased from 15% (1um) to 40% (32nm) – Memory latency gap could eliminate most of the benefits of CPU improvements Petascale challenges (10^15 FLOPS) – Clock frequencies stagnated in the few GHz range – Energy usage and Power wastage of modern HPC systems are becoming a huge economic burden that can not be ignored any longer – Requirements for annual performance improvements grow steadily – Programmers continue to rely on sequential execution (1D approach) For affordable petascale systems  Novel approach is needed

14. x x + 30 y SCSVar x = io.input("x", scsInt(32)); SCSVar result = x * x + 30; io.output("y", result, scsInt(32)); 14 OpenSPL Example: X 2 + 30

15. OpenSPL Example: Moving Average 15 SCSVar x = io.input(“x”, scsFloat(7,17)); SCSVar prev = stream.offset(x, -1); SCSVar next = stream.offset(x, 1); SCSVar sum = prev + x + next; SCSVar result = sum / 3; io.output(“y”, result, scsFloat(7,17)); Y = (X n-1 + X + X n+1 ) / 3

16. OpenSPL Example: Choices 16 x + 1 y - 1 > 10 SCSVar x = io.input(“x”, scsUInt(24)); SCSVar result = (x>10) ? x+1 : x-1; io.output(“y”, result, scsUInt(24));

17. 17 lectures/exercises, Theory and Practice of Computing in Space 17 OpenSPL and MaxAcademy LECTURE 1: Concepts for Computing in Space LECTURE 2: Converting Temporal Code to Graphs LECTURE 3: Computing, Storage and Networking LECTURE 4: OpenSPL LECTURE 5: Dataflow Engines (DFEs) LECTURE 6: Programming DFEs (Basics) LECTURE 7: Programming DFEs (Advanced) LECTURE 8: Programming DFEs (Dynamic and multiple kernels) LECTURE 9: Application Case Studies I LECTURE 10: Making things go fast LECTURE 11: Numerics LECTURE 12: Application Case Studies II LECTURE 13: System Perspective LECTURE 14: Verifying Results LECTURE 15: Performance Modelling LECTURE 16: Economics of Computing in Space LECTURE 17: Summary and Conclusions

18. Maxeler Dataflow Engine Platforms 18 High Density DFEs Intel Xeon CPU cores and up to 6 DFEs with 288GB of RAM The Dataflow Appliance Dense compute with 8 DFEs, 384GB of RAM and dynamic allocation of DFEs to CPU servers with zero-copy RDMA access The Low Latency Appliance Intel Xeon CPUs and 1-2 DFEs with direct links to up to six 10Gbit Ethernet connections

19. 19 Bringing Scalability and Efficiency to the Datacenter

20. 3000³ Modeling *presented at SEG 2010. Compared to 32 3GHz x86 cores parallelized using MPI 8 Full Intel Racks ~100kWatts => 2 MaxNodes (2U) Maxeler System <1kWatt

21. Typical Scalability of Sparse Matrix Visage – Geomechanics (2 node Nehalem 2.93 GHz) Eclipse Benchmark ( 2 node Westmere 3.06 GHz)

22. Given matrix A, vector b, find vector x in: Ax = b Typically memory bound, not parallelisable. 1 MaxNode achieved 20-40x the performance of an x86 node. 22 Sparse Matrix Solving O. Lindtjorn et al, 2010 624 Domain Specific Address and Data Encoding

23.  Equations: Shallow Water Equations (SWEs)  Atmospheric equations Global Weather Simulation [L. Gan, H. Fu, W. Luk, C. Yang, W. Xue, X. Huang, Y. Zhang, and G. Yang, Accelerating solvers for global atmospheric equations through mixed-precision data flow engine, FPL2013]

24. Always double-precision needed?  Range analysis to track the absolute values of all variables fixed-point reduced-precision

25. What about error vs area tradeoffs  Bit accurate simulations for different bit-width configurations.

26. Accuracy validation [Chao Yang, Wei Xue, Haohuan Fu, Lin Gan, et al. ‘A Peta-scalable CPU-GPU Algorithm for Global Atmospheric Simulations’, PPoPP’2013]

27. And there is also performance gain PlatformPerformance () Speedup 6-core CPU4.66K1 Tianhe-1A node110.38K23x MaxWorkstation468.1K100x MaxNode1.54M330x 14x

28. And power efficiency too PlatformEfficiency ( ) Speedup 6-core CPU20.711 Tianhe-1A node306.614.8x MaxWorkstation2.52K121.6x MaxNode3K144.9x 9 x

29. 29 Weather and climate models on DFEs Which one is better? Finer grid and higher precision are obviously preferred but the computational requirements will increase  Power usage  $$ What about using reduced precision? (15 bits instead of 64 double precision FP)

30. 30 Weather models precision comparison

31. 31 What about 15 days of simulation? Surface pressure after 15 days of simulation for the double precision and the reduced precision simulations (quality of the simulation hardly reduced)

32. MAX-UP: Astro Chemistry CPU DFE

33. Does it work? 33 Test problem 2D Linear advection 4 th order Runge-Kutta Regular torus mesh Gaussian bump Bump is advected across the torus mesh After 20 timesteps it should be back where it started Bump at t=20

34. CFD Performance 34 For this 2D linear advection test problem we achieve ca.450M degree-of- freedom updates per second For comparison a GPU implementation (of a Navier- Stokes solver) achieves ca.50M DOFs/s Max3A workstation with Xilinx Virtex 6 475t + 4-core i7

35. CFD Conclusions You really can do unstructured meshes on a dataflow accelerator You really can max out the DRAM bandwidth You really can get exciting performance You have to work pretty hard Or build on the work of others This was not an acceleration project We designed a generic architecture for a family of problems 35

37. 37 We’re Hiring Candidate Profiles Acceleration Architect (UK) Application Engineer (USA) System Administrator (UK) Senior PCB Designer (UK) Hardware Engineer (UK) Networking Engineer (UK) Electronics Technician (UK)