Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. This thesis presents a framework for systematic performance analysis of scalable, heterogeneous applications. Based on event traces, it automatically detects the critical path and inefficiencies that result in waiting or idle time, e.g. due to load imbalances between parallel execution streams. As a prerequisite for the analysis of heterogeneous programs, this thesis specifies inefficiency patterns for computation offloading. Furthermore, an essential contribution was made to the development of tool interfaces for OpenACC and OpenMP, which enable a portable data acquisition and a subsequent analysis for programs with offload directives. At present, these interfaces are already part of the latest OpenACC and OpenMP API specification. The aforementioned work, existing preliminary work, and established analysis methods are combined into a generic analysis process, which can be applied across programming models. Based on the detection of wait or idle states, which can propagate over several levels of parallelism, the analysis identifies wasted computing resources and their root cause as well as the critical-path share for each program region. Thus, it determines the influence of program regions on the load balancing between execution streams and the program runtime. The analysis results include a summary of the detected inefficiency patterns and a program trace, enhanced with information about wait states, their cause, and the critical path. In addition, a ranking, based on the amount of waiting time a program region caused on the critical path, highlights program regions that are relevant for program optimization. The scalability of the proposed performance analysis and its implementation is demonstrated using High-Performance Linpack (HPL), while the analysis results are validated with synthetic programs. A scientific application that uses MPI, OpenMP, and CUDA simultaneously is investigated in order to show the applicability of the analysis

Computational methodology for enhanced sensitivity analysis of gene regulatory networks

Biological computing is held towards a new era of processing platforms based on the bio-logical computer structures that are at the heart of biological systems with information processing capabilities. These bio-logical computer structures are mostly based on gene regulatory networks, mainly because their dynamics reminds the computer logic structures functioning. The use of these bio-structures is still in its early days since they are for the time being far less effective than their silicon counterparts. However, their use can be already exploited for a wide range of applications, covering pharmacological, medical and industrial. In order to develop such applications, a precise design that is based on computational modelling is vital in the process of their implementation. Gene regulatory networks can be described as a chemical reacting systems. The dynamics of such systems is defined at the molecular level with a set of interacting reactions. The stochastic simulation algorithm can be used to generate the time evolution trajectories of each chemical species by firing each reaction according to a Monte-Carlo experiment. The main shortcoming of this approach is its computational complexity, which increases linearly with the total number of reactions that have to be simulated. When the number of reactions becomes too high, the stochastic simulation algorithm turns out to be impracticable. This is the case of certain gene regulatory networks, which can be either found in nature or can be artificially constructed. An additional problem lies in the fact that reactions in such networks can often occur at different time scales, which can differ by many orders of magnitude. Such scenario occurs when gene regulatory networks contain multiple cis-regulatory binding sites, on which different transcription factors are able to bind non-cooperatively. The transcription factors binding occurs much faster than the average reactions in the gene expression, therefore, this time-scale gap needs to be accounted into the simulation. Moreover, the transcription control can be affected by specific dispositions of the bound transcription factors, which is only possible to simulate, if all the reactions that can produce the same dispositions are defined. The number of such reactions increases exponentially with the number of binding sites. In order to decrease the time complexity of the stochastic simulation algorithm for such gene regulatory networks, an alternative algorithm called the dynamic multi-scale stochastic algorithm (DMSSA) is proposed, in which the reactions involved in the transcription regulation can be simulated independently, by performing the stochastic simulation algorithm in a nested fashion. This is conditioned by the property of the set of reactions, describing the gene regulatory network, being divided into two subsets, i.e. a set of "fast" reactions, which occur frequently in a short time scale, and a set of "slow" reactions, which occur less frequently in longer time scales. This thesis demonstrates the equivalence between this approach and the standard stochastic simulation algorithm and shows its capabilities on two gene regulatory models, that are commonly used as examples in systems and synthetic biology. The thesis focuses on how to identify the most important input parameters of multi-scale models, that affect the system the most. This is a common practice during the design of bio-logical structures and can be achieved with the sensitivity analysis. It may be difficult to carry out such analysis for complex reaction networks exhibiting different time scales. In order to cope with this issue, an alternative computation of the elementary effects in the Morris screening method is proposed, which is able to sort all the model parameters, independently on their structural or time scale definitions, in order of importance, i.e. which parameter carries the largest influence on the response of the model. To ease the use of the simulation algorithm and to perform the sensitivity analysis, the thesis presents ParMSSA, an OpenCL based engine for performing parallel stochastic simulations on multi-core architectures. ParMSSA aims to accelerate the simulations, performed with our approach. ParMSSA is capable to run concurrently multiple instances of DMSSA, which are usually needed for reducing the noisy results of stochastic simulations. ParMSSA provides also a framework for performing the Morris screening experiment on reaction networks, which allows users to carry out the sensitivity analysis of observed systems. The simulation results provided by the ParMSSA can be easily interpreted and can be used to assess the robustness of the bio-logical computer structures. The proposed algorithms and the proposed simulation engine were applied on two case studies, i.e. on the Epstein-Barr virus genetic switch and on the synthetic repressilator with multiple transcription factor binding sites. The results of the sensitivity analysis of the repressilator revealed that larger numbers of binding sites increase the robustness of the system and thus the robustness of the oscillatory behaviour

Parallel Model Counting with CUDA: Algorithm Engineering for Efficient Hardware Utilization

Propositional model counting (MC) and its extensions as well as applications in the area of probabilistic reasoning have received renewed attention in recent years. As a result, also the need for quickly solving counting-based problems with automated solvers is critical for certain areas. In this paper, we present experiments evaluating various techniques in order to improve the performance of parallel model counting on general purpose graphics processing units (GPGPUs). Thereby, we mainly consider engineering efficient algorithms for model counting on GPGPUs that utilize the treewidth of a propositional formula by means of dynamic programming. The combination of our techniques results in the solver GPUSAT3, which is based on the programming framework Cuda that -compared to other frameworks- shows superior extensibility and driver support. When combining all findings of this work, we show that GPUSAT3 not only solves more instances of the recent Model Counting Competition 2020 (MCC 2020) than existing GPGPU-based systems, but also solves those significantly faster. A portfolio with one of the best solvers of MCC 2020 and GPUSAT3 solves 19% more instances than the former alone in less than half of the runtime

Astaroth: Ohjelmistokirjasto stensiililaskentaan grafiikkasuorittimilla

Graphics processing units (GPUs) are coprocessors, which offer higher throughput and better power efficiency than central processing units in dataparallel tasks. For this reason, graphics processors provide a good platform for high-performance computing. However, programming GPUs such that all the available performance is utilized requires in-depth knowledge of the architecture of the hardware. Additionally, the problem of high-order stencil computations on GPUs in challenging multiphysics applications has not been adequately explored in previous work. In this thesis, we address these issues by presenting a library, an efficient algorithm and a domain-specific language for solving stencil computations within a structured grid. We tested our implementation by simulating magnetohydrodynamics, which involved the computation of first, second, and cross partial derivatives using second-, fourth-, sixth-, and eight-order finite differences with single and double precision. The running time of our integration kernel was 2.8–9.1 times slower than the theoretical minimum time, which it would take to read the computational domain and write it back to device memory exactly once, without taking into account the effects of finite caches or arithmetic operations on performance. Additionally, we made a performance comparison with a CPU solver widely used for scientific computations, which we benchmarked on a total of 24 cores of two Intel Xeon E5-2690 v3 processors. Our solver, benchmarked on a Tesla P100 PCIe GPU, outperformed the CPU solver by factors of 6.7 and 10.4 when using single and double precision, respectively.Grafiikkasuorittimet ovat apusuorittimia, jotka tarjoavat rinnakkain laskettavissa tehtävissä parempaa suoritus- ja energiatehokkuutta kuin keskussuorittimet. Tästä syystä grafiikkasuorittimet tarjoavat hyvän alustan suurteholaskennan tarpeisiin. Toisaalta grafiikkasuorittimen ohjelmointi siten, että kaikki tarjolla oleva suorituskyky saadaan hyödynnettyä, vaatii syvällistä asiantuntemusta ohjelmoitavan laitteiston arkkitehtuurista. Korkean asteen stensiililaskentaa haastavissa fysiikkasovelluksissa ei ole myöskään tutkittu laajalti aiemmissa julkaisuissa. Tässä työssä otamme kantaa näihin ongelmiin esittelemällä ohjelmistokirjaston, tehokkaan algoritmin, sekä tehtävään räätälöidyn ohjelmointikielen stensiililaskujen ratkaisemiseen säännöllisessä hilassa. Testasimme toteutustamme simuloimalla magnetohydrodynamiikkaa, johon kuului ensimmäisen ja toisen kertaluvun derivaattojen lisäksi ristiderivaattojen ratkaisutoisen, neljännen, kuudennen ja kahdeksannen kertaluvun differenssimenetelmällä käyttäen sekä 32- että 64-bittisiä liukulukuja. Integrointifunktiomme suoritusaika oli 2.8–9.1 kertaa hitaampi kuin teoreettinen vähimmäisajoaika, joka menisi laskennallisen alueen lukemiseen ja kirjoittamiseen apusuorittimen muistista täsmälleen kerran, ottamatta huomioon äärellisen välimuistin tai laskennan vaikutusta suoritusaikaan. Vertasimme kirjastomme suoritusaikaa laajalti tieteellisessä laskennassa käytettyyn keskussuorittimille tarkoitettuun ratkaisijaan, jonka ajoimme kokonaisuudessaan 24:llä ytimellä kahdella Intel Xeon E5-2690 v3 -suorittimella. Tähän ratkaisijaan verrattuna Tesla P100 PCIe -grafiikkasuorittimella ajettu ratkaisijamme oli 6.7 ja 10.4 kertaa nopeampi 32- ja 64-bittisillä liukuluvuilla laskettaessa, tässä järjestyksessä

Architecture of Advanced Numerical Analysis Systems

This unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will Learn Optimize core operations based on N-dimensional arrays Design and implement an industry-level algorithmic differentiation module Implement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiation Design and optimize a computation graph module, and understand the benefits it brings to the numerical computing library Accommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computation Use the Zoo system for efficient scripting, code sharing, service deployment, and composition Design and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance Who This Book Is For Those with prior programming experience, especially with the OCaml programming language, or with scientific computing experience who may be new to OCaml. Most importantly, it is for those who are eager to understand not only how to use something, but also how it is built up

A scalable, efficient scheme for evaluation of stencil computations over unstructured meshes

pre-printStencil computations are a common class of operations that appear in many computational scientific and engineering applications. Stencil computations often benefit from compile-time analysis, exploiting data-locality, and parallelism. Post-processing of discontinuous Galerkin (dG) simulation solutions with B-spline kernels is an example of a numerical method which requires evaluating computationally intensive stencil operations over a mesh. Previous work on stencil computations has focused on structured meshes, while giving little attention to unstructured meshes. Performing stencil operations over an unstructured mesh requires sampling of heterogeneous elements which often leads to inefficient memory access patterns and limits data locality/reuse. In this paper, we present an efficient method for performing stencil computations over unstructured meshes which increases data-locality and cache efficiency, and a scalable approach for stencil tiling and concurrent execution. We provide experimental results in the context of post-processing of dG solutions that demonstrate the effectiveness of our approach

Efficient architectures of heterogeneous fpga-gpu for 3-d medical image compression

The advent of development in three-dimensional (3-D) imaging modalities have generated a massive amount of volumetric data in 3-D images such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). Existing survey reveals the presence of a huge gap for further research in exploiting reconfigurable computing for 3-D medical image compression. This research proposes an FPGA based co-processing solution to accelerate the mentioned medical imaging system. The HWT block implemented on the sbRIO-9632 FPGA board is Spartan 3 (XC3S2000) chip prototyping board. Analysis and performance evaluation of the 3-D images were been conducted. Furthermore, a novel architecture of context-based adaptive binary arithmetic coder (CABAC) is the advanced entropy coding tool employed by main and higher profiles of H.264/AVC. This research focuses on GPU implementation of CABAC and comparative study of discrete wavelet transform (DWT) and without DWT for 3-D medical image compression systems. Implementation results on MRI and CT images, showing GPU significantly outperforming single-threaded CPU implementation. Overall, CT and MRI modalities with DWT outperform in term of compression ratio, peak signal to noise ratio (PSNR) and latency compared with images without DWT process. For heterogeneous computing, MRI images with various sizes and format, such as JPEG and DICOM was implemented. Evaluation results are shown for each memory iteration, transfer sizes from GPU to CPU consuming more bandwidth or throughput. For size 786, 486 bytes JPEG format, both directions consumed bandwidth tend to balance. Bandwidth is relative to the transfer size, the larger sizing will take more latency and throughput. Next, OpenCL implementation for concurrent task via dedicated FPGA. Finding from implementation reveals, OpenCL on batch procession mode with AOC techniques offers substantial results where the amount of logic, area, register and memory increased proportionally to the number of batch. It is because of the kernel will copy the kernel block refer to batch number. Therefore memory bank increased periodically related to kernel block. It was found through comparative study that the tree balance and unroll loop architecture provides better achievement, in term of local memory, latency and throughput