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Sunita Chandrasekaran 1 Oscar Hernandez 2 Douglas Leslie Maskell 1 Barbara Chapman 2 Van Bui 2

Compilation and Parallelization Techniques with Tool Support to Realize Sequence Alignment Algorithm on FPGA and Multicore. Sunita Chandrasekaran 1 Oscar Hernandez 2 Douglas Leslie Maskell 1 Barbara Chapman 2 Van Bui 2 1 Nanyang Technological University, Singapore

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Sunita Chandrasekaran 1 Oscar Hernandez 2 Douglas Leslie Maskell 1 Barbara Chapman 2 Van Bui 2

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  1. Compilation and Parallelization Techniques with Tool Support to Realize Sequence Alignment Algorithm on FPGA and Multicore Sunita Chandrasekaran1 Oscar Hernandez2 Douglas Leslie Maskell1 Barbara Chapman2 Van Bui2 1Nanyang Technological University, Singapore 2University of Houston, HPCTools, Texas, USA

  2. Challenge • Application – Bioinformatics • Proposed Idea • Tool Support • Tuning Methodology • Scheduling • Execution and Tuning Model • Conclusion and Future Work

  3. Reconfigurable Computing – Customizing a computational fabric for specific applications, e.g. FPGA (Field Programmable Gate Array) Reconfigurable Computing and HPC is a reality… Fills the gap between hardware and software FPGA based accelerators – Involving massive parallelism and extensible hardware optimizations Portions of the application can be run on reprogrammable hardware Challenge Important to identify the hot spots in the application to determine which portion to be applicable on the software and which portion on the hardware. Paper presents a tuning methodology to identify the bottlenecks in the program using a parallelizing compiler with the help of static and analysis tools

  4. Application Bioinformatics – Multiple Sequence Alignment Arranging the primary sequences of DNA, RNA or protein to identify the regions of similarity Areas of research in Bioinformatics Sequence Alignment Phylogenetic Tree Protein Folding Gene Structure Prediction Internal small Stretches of Similarity Constructed based on the distances between the sequences Local Global Classification and Identification of genes 2D 3D N-W algorithm S-W algorithm End to End Alignment

  5. Smith Waterman Algorithm • Similar subsequences of two sequences • Implemented by large bioinformatics organizations • Dynamic programming algorithm used to compute local alignment of pair of sequences • Impractical due to time and space complexities • Progressive alignment is the widely used heuristic- distance value between each pair of sequences- phylogenetic tree- pairwise alignment of various profiles • Hardware implementations of the algorithm exploit opportunities for parallelism and further accelerate the execution

  6. Proposed Idea • Efficient C code implementation of the MSA • Preprocessing steps and parallel processing approaches • Profiling to determine the performance bottlenecks, identifying the areas of the code that can benefit from the parallelization • High level optimizations to be performed to obtain a better speed-up Improving the CPI • Including pipelining, data prefetching, data locality, avoiding resource contention and support parallelization of the main kernel

  7. Tool Support OpenUH Compiler Infrastructure Source code w/ OpenMP directives Front-end (C/C++ & Fortran 77/90) IPA (Inter Procedural Analyzer) Portable OpenMP Runtime library LNO (Loop Nest Optimizer) Linking WOPT (global scalar optimizer) Native compilers Executables IR-to-source translation (whir2c & whirl2f) Source code w/ OMP lib calls Backend

  8. The OpenUH Compiler • Based on the Open64 compiler. • A suite of optimizing compiler tools for Linux/Intel IA-64 systems and IA-32 (source-to-source). • First release open-sourced by SGI • Available for researchers/developers in the community. • Multiple languages and multiple targets • C, C++ and Fortran77/90 • OpenMP 2.0 support (University of Houston, Tsinghua University, PathScale)

  9. OpenUH/64 includes The Dragon Analysis Tool Call Graph Array Regions Flow Graph Data Dependence Analysis

  10. TAU- Profiling Toolkit for Performance Analysis of Parallel programs written in Fortran, C, C++, Java or Python

  11. Tuning Methodology • Bottlenecks in the program are identified with hardware performance counters The following are the investigations: • Count of useful instructions = 7.63E+9 • No-opt operations = 44% (moving this portion to the reconfigurable platform would be inefficient) • Branch Mispredictions = 75% (this would stall the pipeline, cause wastage of resources) • Cycles per instruction = 0.3178 (Instructions are stalling)

  12. Goal: To reduce total cycles, reduce stalls, no-ops, conditionals and hoist loops outside, improve memory locality • Used software parallel programming paradigm, OpenMP and pragmas to parallelize the code • Realized the dependencies in the program with Dragon tool • Control Flow and Data Flow graph used to distinguish between regions • Aggressive privatizations applied to most of the arrays • Fine grained locks define to access shared arrays • Hot spots of the application identified

  13. OpenMP Pseudo code msap { #pragma parallel region private(..) firstprivate(..) { #pragma omp for for(…) Initialize Array of Locks #pragma omp for no wait for (…) { for (…) { for (…) { Computations () for (…) { { Computations () } // update to shared data omp_set_lock() Updates to shared data. omp_unset_lock() } }

  14. Result Obtained after performing optimizations: • Count of useful instructions = 8.40E+9 • No-opt operations = 24% • Branch Mispredictions = 59% • Cycles per instruction = 0.28 (Lowered, hence higher performance) CPI improvements of 11.89% - Reduction in branch misprediction of 21.33% - NOP instructions reduced by 45.45%

  15. Scheduling Static Scheduling • Reduced synchronization/communication overhead • Uneven sized tasks • Load imbalances and idle processors leading to wastage of resources • Triangular matrix- resultant matrix not achieved - No ideal speed-up

  16. Dynamic Scheduling • Option of Flexibility • As the parallel loop is executed, number of iterations each thread performs is determined dynamically • Loop divided into chunks of h iterations or chunk size equaling to 1 or x% of the hth iterations. • Ideal speed-up of ~80% achieved

  17. Dynamic Scheduling (Triangular Matrix) Vs Static Scheduling

  18. Execution and Tuning Model

  19. Conclusion and Future Work • Multithreaded application achieves 78% of ideal speed-up on dynamic scheduling with 128 threads on 1000 sequence protein data set. • Looking at translating OpenMP to Impulse-C, a tool for main stream embedded programmers seeking high performance through FPGA co-processing • Plan to address the lack of tools and techniques for turn-key mapping of algorithms to the hybrid CPU-FPGA systems by developing an OpenUH add – on module to perform this mapping automatically

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