Guillermo L. Taboada

jModelTest 2: more models, new heuristics and parallel computing

jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets

ProtTest 3

UNLABELLED We have implemented a high-performance computing (HPC) version of ProtTest that can be executed in parallel in multicore desktops and clusters. This version, called ProtTest 3, includes new features and extended capabilities. AVAILABILITY ProtTest 3 source code and binaries are freely available under GNU license for download from http://darwin.uvigo.es/software/prottest3, linked to a Mercurial repository at Bitbucket (https://bitbucket.org/). CONTACT dposada@uvigo.es SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Performance Evaluation of MPI, UPC and OpenMP on Multicore Architectures

The current trend to multicore architectures underscores the need of parallelism. While new languages and alternatives for supporting more efficiently these systems are proposed, MPI faces this new challenge. Therefore, up-to-date performance evaluations of current options for programming multicore systems are needed. This paper evaluates MPI performance against Unified Parallel C (UPC) and OpenMP on multicore architectures. From the analysis of the results, it can be concluded that MPI is generally the best choice on multicore systems with both shared and hybrid shared/distributed memory, as it takes the highest advantage of data locality, the key factor for performance in these systems. Regarding UPC, although it exploits efficiently the data layout in memory, it suffers from remote shared memory accesses, whereas OpenMP usually lacks efficient data locality support and is restricted to shared memory systems, which limits its scalability.

ProtTest-HPC: fast selection of best-fit models of protein evolution

The use of probabilistic models of amino acid replacement is essential for the study of protein evolution, and programs like ProtTest implement different strategies to identify the best-fit model for the data at hand. For large protein alignments, this task can demand vast computational resources, preventing the justification of the model used in the analysis. We have implemented a High Performance Computing (HPC) version of ProtTest. ProtTest-HPC can be executed in parallel in HPC environments as: (1) a GUI-based desktop version that uses multi-core processors and (2) a cluster-based version that distributes the computational load among nodes. The use of ProtTest-HPC resulted in significant performance gains, with speedups of up to 50 on a high performance cluster.

Java in the High Performance Computing arena: Research, practice and experience

The rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and thorough and up-to-date evaluations of their performance, as well as the unawareness on current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC. This paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, presents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on two shared memory environments and two InfiniBand multi-core clusters. The main conclusions are that: (1) the significant interest in Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field; (2) Java can achieve almost similar performance to natively compiled languages, both for sequential and parallel applications, being an alternative for HPC programming; (3) the recent advances in the efficient support of Java communications on shared memory and low-latency networks are bridging the gap between Java and natively compiled applications in HPC. Thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC.

Performance analysis of HPC applications in the cloud

The scalability of High Performance Computing (HPC) applications depends heavily on the efficient support of network communications in virtualized environments. However, Infrastructure as a Service (IaaS) providers are more focused on deploying systems with higher computational power interconnected via high-speed networks rather than improving the scalability of the communication middleware. This paper analyzes the main performance bottlenecks in HPC application scalability on the Amazon EC2 Cluster Compute platform: (1) evaluating the communication performance on shared memory and a virtualized 10 Gigabit Ethernet network; (2) assessing the scalability of representative HPC codes, the NAS Parallel Benchmarks, using an important number of cores, up to 512; (3) analyzing the new cluster instances (CC2), both in terms of single instance performance, scalability and cost-efficiency of its use; (4) suggesting techniques for reducing the impact of the virtualization overhead in the scalability of communication-intensive HPC codes, such as the direct access of the Virtual Machine to the network and reducing the number of processes per instance; and (5) proposing the combination of message-passing with multithreading as the most scalable and cost-effective option for running HPC applications on the Amazon EC2 Cluster Compute platform. Highlights? Performance results of HPC applications in the cloud using up to 512 cores. ? Up-to-date performance evaluation of the Amazon EC2 Cluster Compute platform. ? High Performance Cloud Computing applications rely on scalable communication. ? Proposal of new techniques for increasing scalability of HPC codes in the cloud. ? Using several levels of parallelism is key for HPC scalability in the cloud.

Java for high performance computing: assessment of current research and practice

The rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and evaluations of their performance, as well as the unawareness of the current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC. This paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, presents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on a multi-core cluster with a high-speed network, InfiniBand, and a 24-core shared memory machine. The main conclusions are that: (1) the significant interest on Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field; and (2) Java can achieve almost similar performance to native languages, both for sequential and parallel applications, being an alternative for HPC programming. Thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC.

jmodeltest.org: selection of nucleotide substitution models on the cloud

Summary: The selection of models of nucleotide substitution is one of the major steps of modern phylogenetic analysis. Different tools exist to accomplish this task, among which jModelTest 2 (jMT2) is one of the most popular. Still, to deal with large DNA alignments with hundreds or thousands of loci, users of jMT2 need to have access to High Performance Computing clusters, including installation and configuration capabilities, conditions not always met. Here we present jmodeltest.org, a novel web server for the transparent execution of jMT2 across different platforms and for a wide range of users. Its main benefit is straightforward execution, avoiding any configuration/execution issues, and reducing significantly in most cases the time required to complete the analysis. Availability and implementation: jmodeltest.org is accessible using modern browsers, such as Firefox, Chrome, Opera, Safari and IE from http://jmodeltest.org. User registration is not mandatory, but users wanting to have additional functionalities, like access to previous analyses, have the possibility of opening a user account. Contact: info@jmodeltest.org

NPB-MPJ: NAS Parallel Benchmarks Implementation for Message-Passing in Java

Java is a valuable and emerging alternative for the development of parallel applications, thanks to the availability of several Java message-passing libraries and its full multithreading support. The combination of both shared and distributed memory programming is an interesting option for parallel programming multi-core systems. However, the concerns about Java performance are hindering its adoption in this field, although it is difficult to evaluate accurately its performance due to the lack of standard benchmarks in Java.This paper presents NPB-MPJ, the first extensive implementation of the NAS Parallel Benchmarks (NPB), the standard parallel benchmark suite, for Message-Passing in Java (MPJ) libraries. Together with the design and implementation details of NPB-MPJ, this paper gathers several optimization techniques that can serve as a guide for the development of more efficient Java applications for High Performance Computing (HPC). NPB-MPJ has been used in the performance evaluation of Java against C/Fortran parallel libraries on two representative multi-core clusters. Thus, NPB-MPJ provides an up-to-date snapshot of MPJ performance, whose comparative analysis of current Java and native parallel solutions confirms that MPJ is an alternative for parallel programming multi-core systems.

Servet: A benchmark suite for autotuning on multicore clusters

The growing complexity in computer system hierarchies due to the increase in the number of cores per processor, levels of cache (some of them shared) and the number of processors per node, as well as the high-speed interconnects, demands the use of new optimization techniques and libraries that take advantage of their features. In this paper Servet, a suite of benchmarks focused on detecting a set of parameters with high influence in the overall performance of multicore systems, is presented. These benchmarks are able to detect the cache hierarchy, including their size and which caches are shared by each core, bandwidths and bottlenecks in memory accesses, as well as communication latencies among cores. These parameters can be used by auto-tuned codes to increase their performance in multicore clusters. Experimental results using different representative systems show that Servet provides very accurate estimates of the parameters of the machine architecture.