Integrating FPGA acceleration into HMMer

Oliver, Tim; Yeow, Leow Yuan; Schmidt, Bertil

doi:10.1016/j.parco.2008.08.003

Cited by 31 publications

(16 citation statements)

References 14 publications

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“…Automatic generation of systolic arrays to implement pipelined evaluation of uniform and affine recurrence equations 9 have been studied and found to give significant speedup for many problems. Numerous accelerators for HMMER and Smith-Waterman exist for which the evaluation time is decreased via a combination of architectural improvements, 8,[10][11][12] data path redesign, [13][14][15][16][17][18] and heuristics. 19,20 To our best knowledge, none of the accelerators mentioned above do rely exclusively on algorithmic improvement techniques as we do.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Rolling partial prefix-sums to speedup evaluation of uniform and affine recurrence equations

Ganesan

Chamberlin

Buhler

et al. 2011

Modeling and Simulation for Defense Systems and Applications VI

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As multithreaded and reconfigurable logic architectures play an increasing role in high-performance computing (HPC), the scientific community is in need for new programming models for efficiently mapping existing applications to the new parallel platforms. In this paper, we show how we can effectively exploit tightly coupled fine-grained parallelism in architectures such as GPU and FPGA to speedup applications described by uniform recurrence equations. We introduce the concept of rolling partial-prefix sums to dynamically keep track of and resolve multiple dependencies without having to evaluate intermediary values. Rolling partial-prefix sums are applicable in low-latency evaluation of dynamic programming problems expressed as uniform or affine equations. To assess our approach, we consider two common problems in computational biology, hidden Markov models (HMMER) for protein motif finding and the Smith-Waterman algorithm. We present a platform independent, linear time solution to HMMER, which is traditionally solved in bilinear time, and a platform independent, sub-linear time solution to Smith-Waterman, which is normally solved in linear time.Keywords: Dynamic Programming, HMMER, Protein-Motif Finding, GPUs, Parallelization, Computational Biology GENERAL ROLLING PARTIAL PREFIX-SUMS ALGORITHMLet D be a finite domain of points. Each point can corresponds to a unique sub-problem or cell in a dynamic programming matrix. Let F be a function from D to a "result" domain Σ (e.g., the real numbers) that corresponds to the computation of the cost point in D. We seek to compute the values F (d) for a point d ∈ D. Let (Σ, ∧) form a commutative semigroup, i.e., the operator ∧ is a commutative, associative binary operator on results.Suppose that F (d) is computable for any d ∈ D as follows:where the summary is the natural extension of ∧ from two to any nonzero number of arguments. The summary operator maps two or more values in the results domain into a single value in the same domain. The function f i (d) is a mapping from multiple (finite number of) points in the domain D to one element in the results domain Σ. Here we consider only monadic recurrences where the function can be written as follows:where ⊕ is a binary extension operator on the results F (d ′ ) and h i (d) ∈ Σ is a "local" function that depends only on d, such as a look-up table and can be computed without the knowledge of any F (d). The relation d ′ < d must be satisfied, according to a partial order <, in order to avoid cyclic dependencies. The minimal elements of the partial order are "base" cases. A subset B of D is said to be "sufficient" for d if every path of dependency from d back to the base cases passes through an element of B. The nature of this dependency imposes a sequential execution of function F as dictated by the partial order. Therefore the number of algorithmic time-steps for sequential execution grows as the size of domain D modulo <, i.e., equal to total number of sets in D such that any two elements from different sets follow t...

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Section: Discussion and Related Workmentioning

confidence: 99%

Rolling partial prefix-sums to speedup evaluation of uniform and affine recurrence equations

Ganesan

Chamberlin

Buhler

et al. 2011

Modeling and Simulation for Defense Systems and Applications VI

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show abstract

“…In [70], an FPGA implementation of HMMER is investigated. As in HMMERCELL, the computationally intensive kernel of the Viterbi algorithm is the main focus.…”

Section: Hmmer Implementationsmentioning

confidence: 99%

Multicore Architectures

2010

Chapman &Amp; Hall/CRC Computational Science

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“…Oliver, et al analyzes the feedback loop (J state) and find out it hinder computing one query in parallel [14]. Instead, they parallelize the design at coarse-grained level that is running several independent query sequences at one time, which also accelerates the task of search in overall running time.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach is simpler: our design can dynamically decide the operation of processing elements (PEs) and parallelize the calculations that don't involve the feedback loop; the states number of Plan7 HMM can be assigned at start up. The work in [8] combines both MPI [7] and FPGA [14] strategies together, and the proposed multi-grained acceleration achieves a reasonable speed-up.…”

Section: Related Workmentioning

confidence: 99%