1. Suitability of Alternative Architectures for Scientific Computing in 5-10 Years LDRD 2002 Strategic-Computational Review July 31, 2001 PIs: Xiaoye Li, Bob Lucas, Lenny Oliker, Katherine Yelick Others: Brian Gaeke, Parry Husbands, Hyun Jin Kim, Hyn Jin Moon

2. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Outline  Project Goals  FY01 progress report  Benchmark kernels definition  Performance on IRAM, comparisons with “conventional” machines  Management plan  Funding opportunities in future

3. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Motivation and Goal  NERSC-3 (now) and NERSC-4 (in 2-3 years) consist of large clusters of commodity SMPs. What about 5-10 years from now?  Future architecture technologies:  PIM (e.g. IRAM, DIVA, Blue Gene)  SIMD/Vector/Stream (e.g. IRAM, Imagine, Playstation)  Low power, narrow data types (e.g., MMX, IRAM, Imagine)  Feasibility of building large-scale systems:  What will the commodity building blocks (nodes and networks) be?  Driven by NERSC and DOE scientific applications codes.  Where do the needs diverge from big market applications?  Influence future architectures

4. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Computational Kernels and Applications  Kernels  Designed to stress memory systems  Some taken from the Data Intensive Systems Stressmarks  Unit and constant stride memory  Transitive-closure  FFT  Dense, sparse linear algebra (BLAS 1 and 2)  Indirect addressing  Pointer-jumping, Neighborhood (Image), sparse CG  NSA Giga-Updates Per Second (GUPS)  Frequent branching a well and irregular memory acess  Unstructured mesh adaptation  Examples of NERSC/DOE applications that may benefit:  Omega3P, accelerator design (SLAC; AMR and sparse linear algebra)  Paratec, material science package (LBNL; FFT and dense linear algebra)  Camille, 3D atmospheric circulation model (preconditioned CG)  HyperClaw, simulate gas dynamics in AMR framework (LBNL)  NWChem, quantum chemistry (PNNL; global arrays and linear algebra)

5. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick VIRAM Overview (UCB) 14.5 mm 20.0 mm  MIPS core (200 MHz)  Single-issue, 8 Kbyte I&D caches  Vector unit (200 MHz)  32 64b elements per register  256b datapaths, (16b, 32b, 64b ops)  4 address generation units  Main memory system  12 MB of on-chip DRAM in 8 banks  12.8 GBytes/s peak bandwidth  Typical power consumption: 2.0 W  Peak vector performance  1.6/3.2/6.4 Gops wo. multiply-add  1.6 Gflops (single-precision)  Same process technology as Blue Gene  But for single chip for multi-media

6. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Status of IRAM Benchmarking Infrastructure  Improved the VIRAM simulator.  Refining the performance model for double-precision FP performance.  Making the backend modular to allow for other microarchitectures.  Packaging the benchmark codes.  Build and test scripts plus input data (small and large data sets).  Added documentation.  Prepare for final chip benchmarking  Tape-out scheduled by UCB for 9/01.

7. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Media Benchmarks  FFT uses in-register permutations, generalized reduction  All others written in C with Cray vectorizing compiler

8. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Power Advantage of PIM+Vectors  100x100 matrix vector multiplication (column layout)  Results from the LAPACK manual (vendor optimized assembly)  VIRAM performance improves with larger matrices!  VIRAM power includes on-chip main memory!

9. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Benchmarks for Scientific Problems  Transitive-closure (small & large data set)  Pointer-jumping (small & large working set)  Computing a histogram  Used for image processing of a 16-bit greyscale image: 1536 x 1536  2 algorithms: 64-elements sorting kernel; privatization  Needed for sorting  Neighborhood image processing (small & large images)  NSA Giga-Updates Per Second (GUPS, 16-bit & 64-bit)  Sparse matrix-vector product:  Order 10000, #nonzeros 177820  2D unstructured mesh adaptation  initial grid: 4802 triangles, final grid: 24010

10. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Benchmark Performance on IRAM Simulator  IRAM (200 MHz, 2 W) versus Mobile Pentium III (500 MHz, 4 W)

11. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Conclusions and VIRAM Future Directions  VIRAM outperforms Pentium III on Scientific problems  With lower power and clock rate than the Mobile Pentium  Vectorization techniques developed for the Cray PVPs applicable.  PIM technology provides low power, low cost memory system.  Similar combination used in Sony Playstation.  Small ISA changes can have large impact  Limited in-register permutations sped up 1K FFT by 5x.  Memory system can still be a bottleneck  Indexed/variable stride costly, due to address generation.  Future work:  Ongoing investigations into impact of lanes, subbanks  Technical paper in preparation – expect completion 09/01  Run benchmark on real VIRAM chips  Examine multiprocessor VIRAM configurations

12. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Project Goals for FY02 and Beyond  Use established data-intensive scientific benchmarks with other emerging architectures:  IMAGINE (Stanford Univ.)  Designed for graphics and image/signal processing  Peak 20 GLOPS (32-bit FP)  Key features: vector processing, VLIW, a streaming memory system. (Not a PIM-based design.)  Preliminary discussions with Bill Dally.  DIVA (DARPA-sponsored: USC/ISI)  Based on PIM “smart memory” design, but for multiprocessors  Move computation to data  Designed for irregular data structures and dynamic databases.  Discussions with Mary Hall about benchmark comparisons

13. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Management Plan  Roles of different groups and PIs  Senior researchers working on particular class of benchmarks  Parry: sorting and histograms  Sherry: sparse matrices  Lenny: unstructured mesh adaptation  Brian: simulation  Jin and Hyun: specific benchmarks  Plan to hire additional postdoc for next year (focus on Imagine)  Undergrad model used for targeted benchmark efforts  Plan for using computational resources at NERSC  Few resourced used, except for comparisons

14. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Future Funding Prospects  FY2003 and beyond  DARPA initiated DIS program  Related projects are continuing under Polymorphic Computing  New BAA coming in “High Productivity Systems”  Interest from other DOE labs (LANL) in general problem  General model  Most architectural research projects need benchmarking  Work has higher quality if done by people who understand apps.  Expertise for hardware projects is different: system level design, circuit design, etc.  Interest from both IRAM and Imagine groups show level of interest

15. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Long Term Impact  Potential impact on Computer Science  Promote research of new architectures and micro- architectures  Understand future architectures  Preparation for procurements  Provide visibility of NERSC in core CS research areas  Correlate applications: DOE vs. large market problems  Influence future machines through research collaborations

16. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick The End

17. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Integer Benchmarks  Strided access important, e.g., RGB  narrow types limited by address generation  Outer loop vectorization and unrolling used  helps avoid short vectors  spilling can be a problem

18. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Status of benchmarking software release Build and test scripts (Makefiles, timing, analysis,...) Standard random number generator Optimized GUPS inner loop GUPS C codes Pointer Jumping Pointer Jumping w/Update Transitive Field Conjugate Gradient (Matrix) Neighborhood Optimized vector histogram code Vector histogram code generator GUPS Docs Test cases (small and large working sets) Optimized Unoptimized  Future work: Write more documentation, add better test cases as we find them Incorporate media benchmarks, AMR code, library of frequently-used compiler flags & pragmas

19. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Status of benchmarking work  Two performance models:  simulator (vsim-p), and trace analyzer (vsimII)  Recent work on vsim-p:  Refining the performance model for double-precision FP performance.  Recent work on vsimII:  Making the backend modular  Goal: Model different architectures w/ same ISA.  Fixing bugs in the memory model of the VIRAM-1 backend.  Better comments in code for better maintainability.  Completing a new backend for a new decoupled cluster architecture.

20. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Comparison with Mobile Pentium  GUPS: VIRAM gets 6 x more GUPS Data element width 16 bit32 bit 64 bit Mobile Pentium GUPS.045.046.036 VIRAM GUPS.295.244 Transitive PointerUpdate VIRAM=30-50% faster than P-III Ex. time for VIRAM rises much more slowly w/ data size than for P-III

21. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Sparse CG  Solve Ax = b; Sparse matrix-vector multiplication dominates.  Traditional CRS format requires:  Indexed load/store for X/Y vectors  Variable vector length, usually short  Other formats for better vectorization:  CRS with narrow band (e.g., RCM ordering)  Smaller strides for X vector  Segmented-Sum (Modified the old code developed for Cray PVP)  Long vector length, of same size  Unit stride  ELL format: make all rows the same length by padding zeros  Long vector length, of same size  Extra flops

22. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick SMVM Performance  DIS matrix: N = 10000, M = 177820 (~ 17 nonzeros per row)  IRAM results (MFLOPS)  Mobile PIII (500 MHz)  CRS: 35 MFLOPS SubBanks1248 CRS91106109110 CRS banded 110 SEG-SUM135154163165 ELL (4.6 X more flops) 511 (111) 570 (124) 612 (133) 632 (137)

23. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick 2D Unstructured Mesh Adaptation  Powerful tool for efficiently solving computational problems with evolving physical features (shocks, vortices, shear layers, crack propagation)  Complicated logic and data structures  Difficult to achieve high efficiently  Irregular data access patterns (pointer chasing)  Many conditionals / integer intensive  Adaptation is tool for making numerical solution cost effective  Three types of element subdivision

24. B. Gaeke, P. Husbands, X. Li, R. Lucas, L. Oliker, K. Yelick Vectorization Strategy and Performance Results Vectorization Strategy and Performance Results  Color elements based on vertices (not edges)  Guarantees no conflicts during vector operations  Vectorize across each subdivision (1:2, 1:3, 1:4) one color at a time  Difficult: many conditionals, low flops, irregular data access, dependencies  Initial grid: 4802 triangles, Final grid 24010 triangles  Preliminary results demonstrate VIRAM 4.5x faster than Mobile Pentium III 500  Higher code complexity (requires graph coloring + reordering) Pentium III 500 1 Lane2 Lanes4 Lanes 61181413 Time (ms)