Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists
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{"title"=>"Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists", "type"=>"journal", "authors"=>[{"first_name"=>"Marco D.", "last_name"=>"Visser", "scopus_author_id"=>"39962936700"}, {"first_name"=>"Sean M.", "last_name"=>"McMahon", "scopus_author_id"=>"7102013897"}, {"first_name"=>"Cory", "last_name"=>"Merow", "scopus_author_id"=>"35763382500"}, {"first_name"=>"Philip M.", "last_name"=>"Dixon", "scopus_author_id"=>"7201937673"}, {"first_name"=>"Sydne", "last_name"=>"Record", "scopus_author_id"=>"19934465400"}, {"first_name"=>"Eelke", "last_name"=>"Jongejans", "scopus_author_id"=>"56034812200"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84926286287", "doi"=>"10.1371/journal.pcbi.1004140", "issn"=>"15537358", "pui"=>"603513896", "isbn"=>"15537358 (Electronic)", "pmid"=>"25811842", "scopus"=>"2-s2.0-84926286287"}, "id"=>"038c1782-5eb5-3b40-8178-579bce073924", "abstract"=>"<p>Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (<italic>aprof</italic>) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (<xref ref-type=\"supplementary-material\" rid=\"pcbi.1004140.s001\">S1</xref>–<xref ref-type=\"supplementary-material\" rid=\"pcbi.1004140.s003\">S3</xref> Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.</p>", "link"=>"http://www.mendeley.com/research/speeding-up-ecological-evolutionary-computations-r-essentials-high-performance-computing-biologists", "reader_count"=>257, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>16, "Researcher"=>76, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>82, "Student > Postgraduate"=>11, "Student > Master"=>33, "Other"=>9, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>16, "Researcher"=>76, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>82, "Student > Postgraduate"=>11, "Student > Master"=>33, "Other"=>9, "Student > Bachelor"=>7, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>19, "Agricultural and Biological Sciences"=>158, "Computer Science"=>7, "Earth and Planetary Sciences"=>7, "Engineering"=>1, "Environmental Science"=>45, "Biochemistry, Genetics and Molecular Biology"=>8, "Mathematics"=>4, "Medicine and Dentistry"=>4, "Psychology"=>1, "Social Sciences"=>1, "Immunology and Microbiology"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>19}, "Environmental Science"=>{"Environmental Science"=>45}, "Engineering"=>{"Engineering"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>7}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>158}, "Computer Science"=>{"Computer Science"=>7}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>16, "Japan"=>2, "United Kingdom"=>8, "Switzerland"=>3, "Portugal"=>2, "Spain"=>6, "Greece"=>1, "New Zealand"=>1, "Canada"=>4, "Netherlands"=>3, "Turkey"=>1, "Belgium"=>1, "China"=>1, "Finland"=>1, "Brazil"=>4, "Denmark"=>1, "Australia"=>2, "France"=>1, "Germany"=>4}, "group_count"=>7}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1990412"], "description"=>"<p>Panels A and B show the execution time as a function of problem size for 10,000 bootstrap resamples conducted on datasets varying in size (A) and time required to run a stochastic population model against the number of time steps (B). \"Naive\" R code, in which no optimizations are applied, uses most computing resources (solid lines in A and B). Optimized R code, with use of efficient functions and optimal data structures pre-allocated in memory (dashed lines in A and B), is faster. In both panels A and B, the largest speed-ups are obtained by using optimal R code (black lines). Subsequent use of parallelism causes further improvement (dot-dashed green line) in A. In panel B, using R's byte compiler improved execution time further above optimal R code (dotted lines in green) while the smallest execution times were achieved by refactoring code in C (red dot-dashed lines). Panels C and D give the computing time (in minutes) needed to conduct the calculations from (C) Merow et al. [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.ref010\" target=\"_blank\">10</a>] and (D) the calculations represented by Fig. 3 in Visser et al. [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.ref011\" target=\"_blank\">11</a>]. Bars in panel C represent the original unaltered code from [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.ref010\" target=\"_blank\">10</a>] (I), the unaltered code run in parallel (II), the revised R code where we replaced a single data.frame with a matrix (III) and the revised code run in parallel (IV). Bars in panel (D) represent the original unaltered code [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.ref011\" target=\"_blank\">11</a>] (I), original run in parallel (II), optimized R code (III), optimized R code using R's byte compiler (IV), optimized R code run in parallel (V), optimized R code using byte compiler run parallel (VI), code with key components refactored in C (VII), and parallel execution of refactored code (VIII). All parallel computations were run on 4 cores, and code is provided in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s001\" target=\"_blank\">S1 Text</a>, section 3, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s002\" target=\"_blank\">S2</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s003\" target=\"_blank\">S3</a> Texts.</p>", "links"=>[], "tags"=>["Biologists Computation", "efficiency problems", "10.5 times", "R code", "review", "technique", "research scope", "High Performance", "Evolutionary Computations", "Information material", "programming challenges", "R package"], "article_id"=>1357317, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Marco D. Visser", "Sean M. McMahon", "Cory Merow", "Philip M. Dixon", "Sydne Record", "Eelke Jongejans"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004140.g002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Execution_time_in_minutes_required_to_complete_various_computational_problems_using_the_optimization_techniques_discussed_/1357317", "title"=>"Execution time in minutes, required to complete various computational problems, using the optimization techniques discussed.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-26 04:10:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1990413"], "description"=>"<p>(<b>A</b>) Realized total speed up when a section of code, taking up a fraction α of the total run time, is improved by a factor <i>I</i> (i.e., the expected program speed-up when the focal section runs <i>I</i> times faster). We see that optimization is only effective when the focal section of code consumes a large fraction of the total run time (α). (B) Total expected speed-up gain for different levels of α as a function of <i>I</i> (e.g., the number of parallel computations). Theoretical limits exist to the maximal improvement in speed, and this is crucially and asymptotically dependent on α—thus code optimization (and investment in computation hardware) are subject to the law of diminishing returns. All predictions here are subject to the scaling of the problem (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s001\" target=\"_blank\">S1 Text</a>, section 2).</p>", "links"=>[], "tags"=>["Biologists Computation", "efficiency problems", "10.5 times", "R code", "review", "technique", "research scope", "High Performance", "Evolutionary Computations", "Information material", "programming challenges", "R package"], "article_id"=>1357318, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Marco D. Visser", "Sean M. McMahon", "Cory Merow", "Philip M. Dixon", "Sydne Record", "Eelke Jongejans"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004140.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_improvements_in_total_program_run_time_using_Amdahl_s_law_/1357318", "title"=>"Projected improvements in total program run time using Amdahl's law.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-26 04:10:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1990410"], "description"=>"<p>In A, B and C, a bootstrap algorithm is shown, and in D, E, and F, a stochastic Lotka-Volterra competition model is shown. The consecutive optimizations described in the text are indicated with the red lines in B, C, E, and F indicating the altered pieces of code. (A) An inefficiently coded bootstrap algorithm, with most time spent in lines 7–8. This algorithm shuffles the values of a large matrix (750,000 x 1000) stored in object \"d\", and then calculates columnwise the difference between the mean column values and the overall mean. (B) A slightly improved code where the overall mean calculation is stored in object \"avg.\" (C) A further improved version of the code where column means are calculated by a specialized and vectorized function (<i>colMeans</i>). (D) A slow running stochastic Lotka-Volterra model of species coexistence that runs a simulation over T years where species have normally distributed intrinsic growth rates (r ∼ Norm(rm,rs)) and competition coefficients (a ∼ Norm(am,as)). (E) the Lotka-Volterra model is more efficient when the pre-allocation-and-fill method is applied. (F) Switching to a matrix to store results further decreases run time. A detailed description of each optimization step with profiling analysis is given online (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s001\" target=\"_blank\">S1 Text</a>, sections 2 and 6).</p>", "links"=>[], "tags"=>["Biologists Computation", "efficiency problems", "10.5 times", "R code", "review", "technique", "research scope", "High Performance", "Evolutionary Computations", "Information material", "programming challenges", "R package"], "article_id"=>1357315, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Marco D. Visser", "Sean M. McMahon", "Cory Merow", "Philip M. Dixon", "Sydne Record", "Eelke Jongejans"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004140.g001", "stats"=>{"downloads"=>1, "page_views"=>39, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualization_of_profiling_output_using_the_aprof_package_for_R_code_where_the_amount_of_time_spent_in_each_line_of_code_is_indicated_by_the_blue_bars_/1357315", "title"=>"Visualization of profiling output using the <i>aprof</i> package for R code, where the amount of time spent in each line of code is indicated by the blue bars.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-26 04:10:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1990414", "https://ndownloader.figshare.com/files/1990415", "https://ndownloader.figshare.com/files/1990416"], "description"=>"<div><p>Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (<i>aprof</i>) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s001\" target=\"_blank\">S1</a>–<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004140#pcbi.1004140.s003\" target=\"_blank\">S3</a> Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.</p></div>", "links"=>[], "tags"=>["Biologists Computation", "efficiency problems", "10.5 times", "R code", "review", "technique", "research scope", "High Performance", "Evolutionary Computations", "Information material", "programming challenges", "R package"], "article_id"=>1357319, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Marco D. Visser", "Sean M. McMahon", "Cory Merow", "Philip M. Dixon", "Sydne Record", "Eelke Jongejans"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004140.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004140.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004140.s003"], "stats"=>{"downloads"=>45, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Speeding_Up_Ecological_and_Evolutionary_Computations_in_R_Essentials_of_High_Performance_Computing_for_Biologists_/1357319", "title"=>"Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-26 04:10:49"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}

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