Center for Programming Models for Scalable Parallel Computing - Towards Enhancing OpenMP for Manycore and Heterogeneous Nodes

OpenMP was not well recognized at the beginning of the project, around year 2003, because of its limited use in DoE production applications and the inmature hardware support for an efficient implementation. Yet in the recent years, it has been graduately adopted both in HPC applications, mostly in the form of MPI+OpenMP hybrid code, and in mid-scale desktop applications for scientific and experimental studies. We have observed this trend and worked deligiently to improve our OpenMP compiler and runtimes, as well as to work with the OpenMP standard organization to make sure OpenMP are evolved in the direction close to DoE missions. In the Center for Programming Models for Scalable Parallel Computing project, the HPCTools team at the University of Houston (UH), directed by Dr. Barbara Chapman, has been working with project partners, external collaborators and hardware vendors to increase the scalability and applicability of OpenMP for multi-core (and future manycore) platforms and for distributed memory systems by exploring different programming models, language extensions, compiler optimizations, as well as runtime library support

Online Modeling and Tuning of Parallel Stream Processing Systems

Writing performant computer programs is hard. Code for high performance applications is profiled, tweaked, and re-factored for months specifically for the hardware for which it is to run. Consumer application code doesn\u27t get the benefit of endless massaging that benefits high performance code, even though heterogeneous processor environments are beginning to resemble those in more performance oriented arenas. This thesis offers a path to performant, parallel code (through stream processing) which is tuned online and automatically adapts to the environment it is given. This approach has the potential to reduce the tuning costs associated with high performance code and brings the benefit of performance tuning to consumer applications where otherwise it would be cost prohibitive. This thesis introduces a stream processing library and multiple techniques to enable its online modeling and tuning. Stream processing (also termed data-flow programming) is a compute paradigm that views an application as a set of logical kernels connected via communications links or streams. Stream processing is increasingly used by computational-x and x-informatics fields (e.g., biology, astrophysics) where the focus is on safe and fast parallelization of specific big-data applications. A major advantage of stream processing is that it enables parallelization without necessitating manual end-user management of non-deterministic behavior often characteristic of more traditional parallel processing methods. Many big-data and high performance applications involve high throughput processing, necessitating usage of many parallel compute kernels on several compute cores. Optimizing the orchestration of kernels has been the focus of much theoretical and empirical modeling work. Purely theoretical parallel programming models can fail when the assumptions implicit within the model are mis-matched with reality (i.e., the model is incorrectly applied). Often it is unclear if the assumptions are actually being met, even when verified under controlled conditions. Full empirical optimization solves this problem by extensively searching the range of likely configurations under native operating conditions. This, however, is expensive in both time and energy. For large, massively parallel systems, even deciding which modeling paradigm to use is often prohibitively expensive and unfortunately transient (with workload and hardware). In an ideal world, a parallel run-time will re-optimize an application continuously to match its environment, with little additional overhead. This work presents methods aimed at doing just that through low overhead instrumentation, modeling, and optimization. Online optimization provides a good trade-off between static optimization and online heuristics. To enable online optimization, modeling decisions must be fast and relatively accurate. Online modeling and optimization of a stream processing system first requires the existence of a stream processing framework that is amenable to the intended type of dynamic manipulation. To fill this void, we developed the RaftLib C++ template library, which enables usage of the stream processing paradigm for C++ applications (it is the run-time which is the basis of almost all the work within this dissertation). An application topology is specified by the user, however almost everything else is optimizable by the run-time. RaftLib takes advantage of the knowledge gained during the design of several prior streaming languages (notably Auto-Pipe). The resultant framework enables online migration of tasks, auto-parallelization, online buffer-reallocation, and other useful dynamic behaviors that were not available in many previous stream processing systems. Several benchmark applications have been designed to assess the performance gains through our approaches and compare performance to other leading stream processing frameworks. Information is essential to any modeling task, to that end a low-overhead instrumentation framework has been developed which is both dynamic and adaptive. Discovering a fast and relatively optimal configuration for a stream processing application often necessitates solving for buffer sizes within a finite capacity queueing network. We show that a generalized gain/loss network flow model can bootstrap the process under certain conditions. Any modeling effort, requires that a model be selected; often a highly manual task, involving many expensive operations. This dissertation demonstrates that machine learning methods (such as a support vector machine) can successfully select models at run-time for a streaming application. The full set of approaches are incorporated into the open source RaftLib framework

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Aceleración de algoritmos de procesamiento de imágenes para el análisis de partículas individuales con microscopia electrónica

Tesis Doctoral inédita cotutelada por la Masaryk University (República Checa) y la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 24-10-2022Cryogenic Electron Microscopy (Cryo-EM) is a vital field in current structural biology. Unlike X-ray crystallography and Nuclear Magnetic Resonance, it can be used to analyze membrane proteins and other samples with overlapping spectral peaks. However, one of the significant limitations of Cryo-EM is the computational complexity. Modern electron microscopes can produce terabytes of data per single session, from which hundreds of thousands of particles must be extracted and processed to obtain a near-atomic resolution of the original sample. Many existing software solutions use high-Performance Computing (HPC) techniques to bring these computations to the realm of practical usability. The common approach to acceleration is parallelization of the processing, but in praxis, we face many complications, such as problem decomposition, data distribution, load scheduling, balancing, and synchronization. Utilization of various accelerators further complicates the situation, as heterogeneous hardware brings additional caveats, for example, limited portability, under-utilization due to synchronization, and sub-optimal code performance due to missing specialization. This dissertation, structured as a compendium of articles, aims to improve the algorithms used in Cryo-EM, esp. the SPA (Single Particle Analysis). We focus on the single-node performance optimizations, using the techniques either available or developed in the HPC field, such as heterogeneous computing or autotuning, which potentially needs the formulation of novel algorithms. The secondary goal of the dissertation is to identify the limitations of state-of-the-art HPC techniques. Since the Cryo-EM pipeline consists of multiple distinct steps targetting different types of data, there is no single bottleneck to be solved. As such, the presented articles show a holistic approach to performance optimization. First, we give details on the GPU acceleration of the specific programs. The achieved speedup is due to the higher performance of the GPU, adjustments of the original algorithm to it, and application of the novel algorithms. More specifically, we provide implementation details of programs for movie alignment, 2D classification, and 3D reconstruction that have been sped up by order of magnitude compared to their original multi-CPU implementation or sufficiently the be used on-the-fly. In addition to these three programs, multiple other programs from an actively used, open-source software package XMIPP have been accelerated and improved. Second, we discuss our contribution to HPC in the form of autotuning. Autotuning is the ability of software to adapt to a changing environment, i.e., input or executing hardware. Towards that goal, we present cuFFTAdvisor, a tool that proposes and, through autotuning, finds the best configuration of the cuFFT library for given constraints of input size and plan settings. We also introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA, together with the introduction of complex dynamic autotuning to the KTT tool. Third, we propose an image processing framework Umpalumpa, which combines a task-based runtime system, data-centric architecture, and dynamic autotuning. The proposed framework allows for writing complex workflows which automatically use available HW resources and adjust to different HW and data but at the same time are easy to maintainThe project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 71367

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Algorithmic and Technical Improvements for Next Generation Drug Design Software Tools

[eng] The pharmaceutical industry is actively looking for new ways of boosting the efficiency and effectiveness of their R&D programmes. The extensive use of computational modeling tools in the drug discovery pipeline (DDP) is having a positive impact on research performance, since in silico experiments are usually faster and cheaper that their real counterparts. The lead identification step is a very sensitive point in the DDP. In this context, Virtual high-throughput screening techniques (VHTS) work as a filtering mecha-nism that benefits the following stages by reducing the number of compounds to be tested experimentally. Unfortunately the simplifications applied in the VHTS docking software make them prone generate false positives and negatives. These errors spread across the rest of the DDP stages, and have a negative impact in terms of financial and time costs. In the Electronic and Atomic Protein Modelling group (Barcelona Supercomputing Center, Life Sciences department), we have developed the Protein Energy Landscape Exploration (PELE) software. PELE has demonstrated to be a good alternative to explore the conformational space of proteins and perform ligand-protein docking simulations. In this thesis we discuss how to turn PELE into a faster and more efficient tool by improving its technical and algorithmic features, so that it can be eventually used in VHTS protocols. Besides, we have addressed the difficulties of analyzing extensive data associated with massive simulation production. First, we have rewritten the software using C++ and modern software engineering techniques. As a consequence, our code base is now well organized and tested. PELE has become a piece of software which is easier to modify, understand, and extend. It is also more robust and reliable. The rewriting the code has helped us to overcome some of its previous technical limitations, such as the restrictions on the size of the systems. Also, it has allowed us to extend PELE with new solvent models, force fields, and types of biomolecules. Moreover, the rewriting has make it possible to adapt the code in order to take advantage of new parallel architectures and accelerators obtaining promising speedup results. Second, we have improved the way PELE handles protein flexibility by im-plemented and internal coordinate Normal Mode Analysis (icNMA) method. This method is able to produce more energy favorable perturbations than the current Anisotropic Network Model (ANM) based strategy. This has allowed us to eliminate the unneeded relaxation phase of PELE. As a consequence, the overall computational performance of the sampling is significantly improved (-5-7x). The new internal coordinates-based methodology is able to capture the flexibility of the backbone better than the old method and is in closer agreement to molecular dynamics than the ANM-based method

The Effect of Code Obfuscation on Authorship Attribution of Binary Computer Files

In many forensic investigations, questions linger regarding the identity of the authors of the software specimen. Research has identified methods for the attribution of binary files that have not been obfuscated, but a significant percentage of malicious software has been obfuscated in an effort to hide both the details of its origin and its true intent. Little research has been done around analyzing obfuscated code for attribution. In part, the reason for this gap in the research is that deobfuscation of an unknown program is a challenging task. Further, the additional transformation of the executable file introduced by the obfuscator modifies or removes features from the original executable that would have been used in the author attribution process. Existing research has demonstrated good success in attributing the authorship of an executable file of unknown provenance using methods based on static analysis of the specimen file. With the addition of file obfuscation, static analysis of files becomes difficult, time consuming, and in some cases, may lead to inaccurate findings. This paper presents a novel process for authorship attribution using dynamic analysis methods. A software emulated system was fully instrumented to become a test harness for a specimen of unknown provenance, allowing for supervised control, monitoring, and trace data collection during execution. This trace data was used as input into a supervised machine learning algorithm trained to identify stylometric differences in the specimen under test and provide predictions on who wrote the specimen. The specimen files were also analyzed for authorship using static analysis methods to compare prediction accuracies with prediction accuracies gathered from this new, dynamic analysis based method. Experiments indicate that this new method can provide better accuracy of author attribution for files of unknown provenance, especially in the case where the specimen file has been obfuscated

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum