SpECTRE: A Task-based Discontinuous Galerkin Code for Relativistic Astrophysics

We introduce a new relativistic astrophysics code, SpECTRE, that combines a discontinuous Galerkin method with a task-based parallelism model. SpECTRE's goal is to achieve more accurate solutions for challenging relativistic astrophysics problems such as core-collapse supernovae and binary neutron star mergers. The robustness of the discontinuous Galerkin method allows for the use of high-resolution shock capturing methods in regions where (relativistic) shocks are found, while exploiting high-order accuracy in smooth regions. A task-based parallelism model allows efficient use of the largest supercomputers for problems with a heterogeneous workload over disparate spatial and temporal scales. We argue that the locality and algorithmic structure of discontinuous Galerkin methods will exhibit good scalability within a task-based parallelism framework. We demonstrate the code on a wide variety of challenging benchmark problems in (non)-relativistic (magneto)-hydrodynamics. We demonstrate the code's scalability including its strong scaling on the NCSA Blue Waters supercomputer up to the machine's full capacity of 22,380 nodes using 671,400 threads.Comment: 41 pages, 13 figures, and 7 tables. Ancillary data contains simulation input file

Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra

The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Implementation of Parallel Search Algorithm in Computational Biology

Bioinformatics and Computational Biology are rapidly growing multidisciplinary fields, which includes wide variety of domains from DNA sequencing to sequence alignments. Recent advances in both these disciplines have allowed biologists all around the world to quickly gather a huge amount of DNA sequence data for analysis. DNA sequence alignments are becoming ever more popular due their impact in early disease diagnosis, in drug engineering, as well as in criminal investigations. With the vast growth and popularity of biological data, searching for a DNA sequence of interest in huge databases is not an easy task to produce results within a realistic time, hence there is a need to enhance the efficiency. The reason why such information is so popular is because biologists can identify genetic information by finding sequences of similar genes or proteins with known behavior or structure without requiring long and expensive laboratory experiments. One of the most widely used tools for performing searches is Basic Local Alignment Search Tool (BLAST), a program for performing pairwise sequence alignments. As the BLAST program becomes ever more popular with biologists around the world, it faces numerous challenges. One of the main challenges is the issue of performance. The BLAST program has been looked at by researchers on how to improve the speed of search by reducing overhead costs. One of the ways to reduce the overhead cost is to incorporate parallelism to improve the performance of the BLAST algorithm. For this paper, I explored existing variations of parallel implementations of the BLAST algorithm and compared its performance improvements with that of serial implementation of BLAST. The speed-up efficiency noted by the parallel program is far greater compared to the serial program. The paper sheds light on the impact of parallelization of the BLAST algorithm and the advantages it has on the overall field of computational biology

Exact parallel alignment of megabase genomic sequences with tunable work distribution

Sequence Alignment is a basic operation in Bioinformatics that is performed thousands of times, on daily basis. The exact methods for pairwise alignment have quadratic time complexity. For this reason, heuristic methods such as BLAST are widely used. To obtain exact results faster, parallel strategies have been proposed but most of them fail to align huge biological sequences. This happens because not only the quadratic time must be considered but also the space should be reduced. In this paper, we evaluate the performance of Z-align, a parallel exact strategy that runs in user-restricted memory space. Also, we propose and evaluate a tunable work distribution mechanism. The results obtained in two clusters show that two sequences of size 24MBP (Mega Base Pairs) and 23MBP, respectively, were successfully aligned with Z-align. Also, in order to align two 3MBP sequences, a speedup of 34.35 was achieved for 64 processors. The evaluation of our work distribution mechanism shows that the execution times can be sensibly reduced when appropriate parameters are chosen. Finally, when comparing Z-align with BLAST, it is clear that, in many cases, Z-align is able to produce alignments with higher score

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics

Genomic and functional characterization of G protein-coupled receptors in the human pathogen Schistosoma mansoni and the model planarian Schmidtea mediterranea

G protein-coupled receptors (GPCRs) constitute the largest known superfamily of integral membrane proteins, and represent a particularly lucrative set of chemotherapeutic targets. These seven transmembrane receptors play a central role in eukaryotic signal transduction and physiology, mediating cellular responses to a diverse range of extracellular stimuli. The phylum Platyhelminthes is of considerable medical and biological importance, housing prominent human pathogens as well as established model organisms in the realm of developmental and stem cell biology. There exists ample motivation to elucidate the structural and functional properties of GPCRs in this phylum. The availability of whole genome sequence data for the human blood fluke Schistosoma mansoni and the model planarian Schmidtea mediterranea paves the way for the first genome-wide analyses of platyhelminth GPCRs. Extensive efforts were made to delineate the receptor complements of these organisms. Further work primarily focuses on validation of a novel method for elucidating receptor function in the native cell membrane environment. Together, these genomic and functional data improve our understanding of basic platyhelminth receptor biology and shed light on a promising set of anthelmintic drug targets. Application of a transmembrane-focused it in silico protocol led to the discovery of 116 S. mansoni and 333 S. mediterranea GPCRs, followed by extensive curation of underlying gene models. Phylogenetic analysis of the resulting dataset confirmed the presence of the primary metazoan GRAFS families and revealed novel lineage-specific receptor groupings, including a large platyhelminth-specific Rhodopsin-like subfamily (PROF1) and a planarian-specific Adhesion-like family (PARF1). Support vector machines (SVMs) were trained and used for ligand-based classification of full-length Rhodopsin GPCRs, complementing phylogenetic and homology-based classification. PROF1 receptors were further revealed as neuronally-expressed endoGPCRs via whole mount in situ hybridization. In light of the unreliable nature of heterologous approaches to GPCR deorphanization, a novel loss-of-function assay was developed for ascertaining the ligand and G protein coupling properties of GPCRs in their native cell membrane environment. RNA interference (RNAi) was used in conjunction with a cAMP radioimmunoassay (RIA) to monitor second messenger modulation in response to the translational suppression of individual receptors. This strategy was applied to the deorphanization of both neuropeptide and aminergic GPCRs, allowing for the determination of Gαs and Gαi/o-mediated signaling. Loss-of-function phenotypic assays were performed in parallel. While these results establish the potential of this approach, future work can lead to further optimizations and the eventual adaptation of this protocol to higher-throughput platforms

The cosmological simulation code GADGET-2

We discuss the cosmological simulation code GADGET-2, a new massively parallel TreeSPH code, capable of following a collisionless fluid with the N-body method, and an ideal gas by means of smoothed particle hydrodynamics (SPH). Our implementation of SPH manifestly conserves energy and entropy in regions free of dissipation, while allowing for fully adaptive smoothing lengths. Gravitational forces are computed with a hierarchical multipole expansion, which can optionally be applied in the form of a TreePM algorithm, where only short-range forces are computed with the `tree'-method while long-range forces are determined with Fourier techniques. Time integration is based on a quasi-symplectic scheme where long-range and short-range forces can be integrated with different timesteps. Individual and adaptive short-range timesteps may also be employed. The domain decomposition used in the parallelisation algorithm is based on a space-filling curve, resulting in high flexibility and tree force errors that do not depend on the way the domains are cut. The code is efficient in terms of memory consumption and required communication bandwidth. It has been used to compute the first cosmological N-body simulation with more than 10^10 dark matter particles, reaching a homogeneous spatial dynamic range of 10^5 per dimension in a 3D box. It has also been used to carry out very large cosmological SPH simulations that account for radiative cooling and star formation, reaching total particle numbers of more than 250 million. We present the algorithms used by the code and discuss their accuracy and performance using a number of test problems. GADGET-2 is publicly released to the research community.Comment: submitted to MNRAS, 31 pages, 20 figures (reduced resolution), code available at http://www.mpa-garching.mpg.de/gadge