This research was supported by NIH grant RR020209-01, “FPGA-Based Computational Accelerators.”

1. Semantic Gap Value of FPGA acceleration Cost of FPGA design BOSTON UNIVERSITY FPGA capacity increases → LAMP: A Tool Suite for Families of FPGA-based Computation Accelerators Tom VanCourt Martin Herbordt • Create a model with behavior left as parameter to be provided. • Logic designer provides • Annotated VHDLReusable control and interface components • App AbstractionInterface definition of application classes and operations • HW AbstractionAbstract definition of FPGA hardware resources • HW Concretion Actual resources present in the FPGA platform • Application specialist provides • App Concretion Actual definitions specific to the application instance • Model Instance Generic accelerator bound to specific HW and application logic • Implement applications as families. • Case study: Dynamic Programming for Approximate String Matching – Choose: • Character by character alignment or goodness-of-match only • Global alignment (with end-rule options) or local, gap parameters • Character type • DNA [2 bits] IUPAC wildcards [4] • Amino acid [5] Codons [6] • Ascii text [8] Unicode 3.0 text [16] • Mismatch scoring, may be parameterized • Accelerator design isn’t logic design. • Accelerators require skilled logic AND requires domain specialists design for high performance for tailoring to details of • specific applications. Model instance Accelerator model for application family Annotated VHDL components HwAbstraction HwConcretion AppConcretion AppAbstraction Result Type Score Only Alignment AlignmentType Local (Smith-Waterman) Global (Needleman-Wunsch) The semantic gap isn’t going away. CharacterType Subclassing creates application- specific data types and behaviors. Nucleotide Amino acid … Codon Wildcard Concrete definition (partial) Abstract definition of character type class IUPAC extends CharType { type Ref {bool: a, c, g, t}; type Que int 0 .. 3; type Score int -1000 .. 1000; const Score scoreZero = 0; match = +1, miss = -10; Score compare(Ref r, Que q) { bool isMatch = (r.a & q==0) | (r.c & q==1) | (r.g & q==2) | (r.t & q==3); … class CharType { abstract type Ref, Que, Score; abstract Score compare( Ref refCh, Que queryCh); abstract const Score zeroScore; } PAM-N BLOSUM-N … ExactMatch Gonnet Compiledcode Machine code C++, Java DomainKnowledge Synthesizedlogic Gates Automated replication makes maximum use of FPGA fabric. Semantic complexity increases→ • Application-specific implementation • can give acceleration > 100. • Application acceleration • Xilinx VP70 Virtex-II Pro • relative to • 3GHz Intel Xeon • Every different application gets individually tuned performance. • Simple applications don’t have to run at ‘worst case’ speed. • Approximate matching application family: • Each component varies individually • Combinatorics work in our favor • Each user creates new possibilities! Smaller PEs - Higher parallelism • FPGAs are near a crossing point. Larger fabric - Increased computing capacity Larger PEs - Don’t constrain other implementations • 2 result types •  17 alignment types •  15 character types • 510 different accelerators • created on demand This research was supported by NIH grant RR020209-01, “FPGA-Based Computational Accelerators.” * M. Gokhale, J. Stone, J. Arnold, and M. Kalinowski. Stream-oriented FPGA computing in the Streams-C high-level language. Proc. FCCM. 2000