Evaluating the use of GPUs in liver image segmentation and HMMER database searches

Walters, John Paul; Balu, Vidyananth; Kompalli, Suryaprakash; Chaudhary, Vipin

doi:10.1109/ipdps.2009.5161073

Cited by 50 publications

(40 citation statements)

References 13 publications

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“…A significant speedup is obtained compared to other popular implementation on the same architecture. 7 The novelty of our work is in the proposed algorithmic improvement. Prefix-sums have been applied to bio-sequence comparisons 8 for parallelizing evaluations of rows.…”

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

View full text Add to dashboard Cite

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“…During recent years, many reports on CUDA implementations of a large variety of segmentation algorithms have been published. Some examples are GPU acceleration of graph cuts (Vineet and Narayanan, 2008), expectation maximization and k-means clustering for analysis of histopathological images of neuroblastoma (Ruiz et al, 2008), registration-based segmentation of MRI volumes (Han et al, 2009), liver segmentation based on Markov random fields (Walters et al, 2009), shape models for segmentation of vertebra in X-ray images (Mahmoudi et al, 2010), random walks (Collins et al, 2012), fuzzy connected image segmentation of CT and MRI volumes (Zhuge et al, 2011) and a hybrid approach to segmentation of vessel laminae from confocal microscope images (Narayanaswamy et al, 2010).…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

351

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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“…HMMER has received a lot of attention from the high performance computing community, with several implementations either for standard parallel machines or more heterogeneous architectures [9][10][11]. In the following we will focus on hardware implementation targeting ASIC or FPGA technology.…”

Section: Early Implementationsmentioning

confidence: 99%

Combining execution pipelines to improve parallel implementation of HMMER on FPGA

Abbas

Derrien

Rajopadhye

et al. 2015

Microprocessors and Microsystems

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International audienceHMMER is a widely used tool in bioinformatic, based on the Profile Hidden Markov Models. The computation kernels of HMMER, namely MSV and P7Viterbi are very compute intensive, and their data dependencies if interpreted naively, lead to a purely sequential execution. In this paper, we propose a original parallelization scheme for HMMER by rewriting the mathematical formulation, to expose hidden potential parallelization opportunities. Our parallelization scheme targets FPGA technology, and our architecture can achieve 10 times speedup compared with the latest HMMER3 SSE version, without compromising on the sensitivity of original algorithm

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Evaluating the use of GPUs in liver image segmentation and HMMER database searches

Cited by 50 publications

References 13 publications

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

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

Medical image processing on the GPU – Past, present and future

Combining execution pipelines to improve parallel implementation of HMMER on FPGA

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