Accelerating the HMMER sequence analysis suite using conventional processors

Abstract

Due to the ever-increasing size of sequence databases it has become clear that faster techniques must be employed to effectively perform biological sequence analysis in a reasonable amount of time. Exploiting the inherent parallelism between sequences is a common strategy. In this paper we enhance both the fine-grained and coursegrained parallelism within the HMMER [2] sequence analysis suite. Our strategies are complementary to one another and, where necessary, can be used as drop-in replacements to the strategies already provided within HMMER. We use conventional processors (Intel Pentium IV Xeon) as well as the freely available MPICH parallel programming environment [1]. Our results show that the MPICH implementation greatly outperforms the PVM HMMER implementation, and our SSE2 implementation also lends greater computational power at no cost to the user.

Accelerating HMMer on FPGAs using systolic array based architecture

“…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.…”

Improving MPI-HMMER's scalability with parallel I/O

“…In addition to our existing implementation, MPI-HMMER [17,8], there has been a variety of work in accelerating HMMER. The most closely related implementation is the IBM Bluegene/L work performed by Jiang et al [9].…”

“…MPI-HMMER is a freely available MPI implementation of the HMMER sequence analysis suite [17,8]. MPI-HMMER is used in thousands of research labs around the world [12,11].…”

Multicore Architectures

“…MPI-HMMER was created to take advantage of computer clusters [96]. Similar to HMMERCELL, one node is assigned a manager role and the rest of the machines are workers over which the workload is distributed.…”