PBPI: a High Performance Implementation of Bayesian Phylogenetic Inference

Feng, Xikang; Cameron,; Buell,

doi:10.1109/sc.2006.47

Cited by 21 publications

(19 citation statements)

References 33 publications

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“…In our previous PBPI work, we validated its correctness and performance at scales less up to 64 nodes [15]. We showed the sequential version of PBPI is up to 19 times faster than its best competitor, MrBayes [6], and up to 46 times faster on 64 nodes for a benchmark dataset of 218 taxa and sequence length of 10,000 characters.…”

Section: The Parallel Strategies Of Pbpimentioning

confidence: 88%

Building the Tree of Life on Terascale Systems

Feng

Cameron

Sosa

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Bayesian phylogenetic inference is an important alternative to maximum likelihood-based phylogenetic method. However, inferring large trees using the Bayesian approach is computationally demanding-requiring huge amounts of memory and months of computational time. With a combination of novel parallel algorithms and latest system technology, terascale phylogenetic tools will provide biologists the computational power necessary to conduct experiments on very large dataset, and thus aid construction of the tree of life.In this work we evaluate the performance of PBPI, a parallel application that reconstructs phylogenetic trees using MCMC-based Bayesian methods, on two terascale systems, Blue Gene/L at IBM Rochester and System X at Virginia Tech. Our results confirm that for a benchmark dataset with 218 taxa and 10000 characters, PBPI can achieve linear speedup on 1024 or more processors for both systems. IntroductionPhylogeny, a tree or network-like structure representing the evolutionary relationship among a group of species, serves as an important framework to organize, compare, and analyze biological data. Besides its primary role in understanding biological evolution and diversity, it has also been widely used in many other areas including genetics, genomics, drug discovery, plant improvement, and disease control. The importance of phylogeny to science and society can be best demonstrated by the NSF ATOL project [1], whose goal is to provide an overall framework for retrieving, comparing, and predicating huge amounts of biological data by "assembling a tree of life for 1.7 million described species on the earth".The fundamental task of most phylogenetic inference is to estimate the "correct" phylogenetic trees given one or multiple data sets which encode the clues for the evolutionary path. Among various phylogenetic inference approaches, the Bayesian approach distinguishes itself in several aspects. First, it uses explicit models of evolution and likelihood functions similar to maximum likelihood estimation, another important statistical phylogenetic method. The Bayesian approach has the potential to incorporate complicated models and existing knowledge into the process of phylogenetic inference. Second, it takes a probabilistic view of the estimated trees and ranks these trees with a quantity called posterior probability. Bayesian phylogenetic inference avoids the baffle present in many NP-hard optimality methods that output one "best" tree.Building large phylogenetic trees using Bayesian approach is computationally demanding. For example, building a phylogenetic tree with hundreds of taxa and thousands of characters may require several gigabytes of memory usage and several months of computing time. To make Bayesian phylogenetic inference more efficient and more practical for large phylogenetic problems, it is necessary to run phylogenetic tools on terascale systems.The main contributions of this paper are in two folds. First, we provide the excellent scaling results of PBPI, a parallel Bayesian phylo...

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Section: The Parallel Strategies Of Pbpimentioning

confidence: 88%

Building the Tree of Life on Terascale Systems

Feng

Cameron

Sosa

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

Self Cite

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“…We evaluate our proposal using a set of scientific benchmarks including PBPI, a parallel implementation of Bayesian phylogenetic inference method for DNA Benchmark Input size T. creation T. duration histogram 256KB 18µs 546µs matmul 128KB 14µs 631µs reduction 256KB 17µs 145µs LU 128KB 16µs 1000µs PBPI 200KB 13µs 114µs jacobi 258KB 15µs 245µs MD5 512KB 14µs 2021µs Table 2: Benchmarks evaluated, average task input size, average task creation overhead and average execution time per task sequence data [16], an implementation of the MD5 hashing algorithm, and a set of kernels representing algorithms commonly found on scientific applications. The full list can be found on Table 2.…”

Section: Workloadsmentioning

confidence: 99%

Adaptive Runtime-Assisted Block Prefetching on Chip-Multiprocessors

García

Rico

Villavieja

et al. 2016

Int J Parallel Prog

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Memory stalls are a significant source of performance degradation in modern processors. Data prefetching is a widely adopted and well studied technique used to alleviate this problem. Prefetching can be performed by the hardware, or be initiated and controlled by software. Among software controlled prefetching we find a wide variety of schemes, including runtimedirected prefetching and more specifically runtime-directed block prefetching.This paper proposes a hybrid prefetching mechanism that integrates a software driven block prefetcher with existing hardware prefetching techniques. Our runtime-assisted software prefetcher brings large blocks of data on-chip with the support of a low cost hardware engine, and synergizes with existing hardware prefetchers that manage locality at a finer granularity. The runtime system that drives the prefetch engine dynamically selects which cache to prefetch to.Our evaluation on a set of scientific benchmarks obtains a maximum speed up of 32% and 10% on average compared to a baseline with hardware prefetching only. As a result, we also achieve a reduction of up to 18% and 3% on average in energy-to-solution.

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“…With the exception of PBPI [4], that conducts multigrain Bayesian inference on the BlueGene/L, to the best of our knowledge, no other work has addressed the issue of parallelizing the PLF. PBPI essentially represents a proofof-concept work, since the capabilities of the program do not correspond to the needs of Biologists for real-world analyses, mainly, because it only implements the very simple models of nucleotide substitution (see [8] for more details).…”

Section: Related Workmentioning

confidence: 99%

New records, replacements, reinstatements and four new species in the Radula parvitexta and R. ventricosa species groups (Jungermanniopsida) in Australia: cases of mistaken identity

Renner

Devos

Brown

et al. 2013

Aust. Systematic Bot.

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We are currently faced with the situation where applications have increasing computational demands and there is a wide selection of parallel processor systems. In this paper we focus on exploiting fine-grain parallelism for a demanding Bioinformatics application -MrBayes -and its Phylogenetic Likelihood Functions (PLF) using different architectures. Our experiments compare side-by-side the scalability and performance achieved using general-purpose multi-core processors, the Cell/BE, and Graphics Processor Units (GPU). The results indicate that all processors scale well for larger computation and data sets. Also, GPU and Cell/BE processors achieve the best improvement for the parallel code section. Nevertheless, data transfers and the execution of the serial portion of the code are the reasons for their poor overall performance. The general-purpose multi-core processors prove to be simpler to program and provide the best balance between an efficient parallel and serial execution, resulting in the largest speedup.

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PBPI: a High Performance Implementation of Bayesian Phylogenetic Inference

Cited by 21 publications

References 33 publications

Building the Tree of Life on Terascale Systems

Building the Tree of Life on Terascale Systems

Adaptive Runtime-Assisted Block Prefetching on Chip-Multiprocessors

New records, replacements, reinstatements and four new species in the Radula parvitexta and R. ventricosa species groups (Jungermanniopsida) in Australia: cases of mistaken identity

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