Multithreaded Processor Research Articles

ЧИСЛЕННОЕ ИССЛЕДОВАНИЕ ДИНАМИКИ ВНУТРИКАМЕРНЫХ ПРОЦЕССОВ В МНОГОСОПЛОВОМ РАКЕТНОМ ДВИГАТЕЛЕ НА ТВЕРДОМ ТОПЛИВЕ ЧАСТЬ 1. МЕТОДИКА РАСЧЁТА

The dynamics of transient in-chamber processes of booster (overclocking) multi-hop of solid rocket motor is investigated. The research method is the formulation of a computational experiment. The conjugate formulation of the problem is considered, which includes: – the ignition device is triggered (the combustion rate of the igniting composition is described on the basis of an experimental and theoretical approach that takes into account the burning of combustion products behind the body of the igniting device); – heating, ignition and subsequent unsteady and turbulent combustion of a charge of a mixed solid fuel (a quasihomogeneous combustion model is used, formulated on the basis of the equations of thermal conductivity and chemical kinetics recorded for the condensed phase (solid fuel), taking into account the influence of the gas phase (flare) on the combustion process in the condensed phase; the method of solving the problem is the finite difference method); – unsteady three-dimensional homogeneous-heterogeneous three-phase flow of nitrogen (pre-pressurization gas of the combustion chamber) and combustion products of the igniting composition and mixed solid fuel in the combustion chamber, multi-core block and behind the multi-core block of the rocket engine (approaches of mechanics of continuous multiphase media are used; the basic system of equations is a system of vortex differential equations of gas dynamics; the solution method is multiparametric a class of difference splitting schemes by physical processes of the Davydov method); – depressurization of the combustion chamber and the departure of the plugs of the multi-nozzle block of solid rocket motor (the equation of motion of the nozzle block plug is Newton's second law; the method of solving the problem is the Euler method). Each of the subtasks is considered in relation and resolved simultaneously – at one-time step. To solve the formulated problem, a set of application programs has been developed using (for the main calculation module) the OpenCL multithreading information processing standard. The operability of the software product was checked.

Speculative inter-thread store-to-load forwarding in SMT architectures

Applications running on out-of-order cores have benefited for decades of store-to-load forwarding which accelerates communication of store values to loads of the same thread. Despite threads running on a simultaneous multithreading (SMT) core could also access the load queues (LQ) and store queues (SQ) / store buffers (SB) of other threads to allow inter-thread store-to-load forwarding, we have skipped exploiting it because if we allow communication of different SMT threads via their LQs and SQs/SBs, write atomicity may be violated with respect to the outside world beyond the acceptable model of read-own-write-early multiple-copy atomicity (rMCA).In our prior work, we leveraged this idea to propose inter-thread store-to-load forwarding (ITSLF). ITLSF accelerates synchronization and communication of threads running in a simultaneous multi-threading processor by allowing stores in the store-queue of a thread to forward data to loads of another thread running in the same core without violating rMCA.In this work, we extend the original ITSLF mechanism to allow inter-thread forwarding from speculative stores (Spec-ITSLF). Spec-ITSLF allows forwarding store values to other threads earlier, which further accelerates synchronization. Spec-ITSLF outperforms a baseline SMT core by 15%, which is 2% better on average (and up to 5% for the TATP workload) than the original ITSLF mechanism. More importantly, Spec-ITSLF is on par with the original ITSLF mechanism regarding storage overhead but does not need to keep track of the speculative state of stores, which was an important source of overhead and complexity in the original mechanism.

Adaptation of Range-Doppler Algorithm for Efficient Beamforming of Monostatic and Multistatic Ultrasound Signals.

Algorithmic changes that increase beamforming speed have become increasingly relevant to processing synthetic aperture (SA) ultrasound data. In particular, beamforming SA data in a spatio-temporal frequency domain using the F-k (Stolt) migration have been shown to reduce the beamforming time by up to two orders of magnitude compared with the conventional delay-and-sum (DAS) beamforming, and it has been used in applications where large amounts of raw data make real-time frame rates difficult to attain, such as multistatic SA imaging and plane-wave Doppler imaging with large ensemble lengths. However, beamforming signals in a spatio-temporal Fourier space can require loading large blocks of data at once, making it memory-intensive and less suited for parallel (i.e., multithreaded) processing. As an alternative, we propose beamforming in a range-Doppler (RD) frequency domain using the range-Doppler algorithm (RDA) that has originally been developed for SA radar (SAR) imaging. Through simulation and phantom experiments, we show that RDA achieves similar lateral resolution and contrast compared with DAS and F-k migration. At the same time, higher axial sidelobes in RDA images can be reduced via (temporal) frequency binning. Like the F-k migration, RDA significantly reduces the overall number of computations relative to DAS, and it achieves ten times lower processing time on a single CPU. Because RDA uses only a spatial Fourier transform (FT), it requires two times less memory than the F-k migration to process the simulated multistatic data and can be executed on as many as a thousand parallel threads (compared with eight parallel threads for the F-k migration), making it more suitable for implementation on modern graphics processing units (GPUs). While RDA is not as parallelizable as DAS, it is expected to hold a significant speed advantage on devices with moderate parallel processing capabilities (up to several thousand cores), such as point-of-care and low-cost ultrasound devices.

From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care

Here we describe the design and implementation of a novel data management platform for an academic cancer center which meets the needs of multiple stakeholders. A small, cross-functional technical team identified key challenges to creating a broad data management and access software solution: lowering the technical skill floor, reducing cost, enhancing user autonomy, optimizing data governance, and reimagining technical team structures in academia. The Hyperion data management platform was designed to meet these challenges in addition to usual considerations of data quality, security, access, stability, and scalability. Implemented between May 2019 and December 2020 at the Wilmot Cancer Institute, Hyperion includes a sophisticated custom validation and interface engine to process data from multiple sources, storing it in a database. Graphical user interfaces and custom wizards permit users to directly interact with data across operational, clinical, research, and administrative contexts. The use of multi-threaded processing, open-source programming languages, and automated system tasks (normally requiring technical expertise) minimizes costs. An integrated ticketing system and active stakeholder committee support data governance and project management. A co-directed, cross-functional team with flattened hierarchy and integration of industry software management practices enhances problem solving and responsiveness to user needs. Access to validated, organized, and current data is critical to the functioning of multiple domains in medicine. Although there are downsides to developing in-house customized software, we describe a successful implementation of custom data management software in an academic cancer center.

Fast parallel implementation for total variation constrained algebraic reconstruction technique.

In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.

High performance multi-platform computing for large-scale image-based finite element modeling of bone

BackgroundImage-based finite element (FE) modeling of bone is a non-invasive method to estimate bone stiffness and strength. High-resolution imaging data as input allows for inclusion of bone microarchitecture but results in large amounts of data unsuitable for traditional FE solvers. Bone-specific mesh-free solvers have been developed over the past 20 years to improve on memory efficiency in simulated bone loading applications. The objective of this study was to provide linear performance benchmarking for a bone-specific, mesh-free solver (FAIM) using µCT and HR-pQCT image data on Mac, Linux, and Windows operating systems using both single- and multi-thread CPU and GPU processing. MethodsThe focus is on the linear gradient-descent solver using standardized uniaxial loading of bone models from µCT, and first- and second-generation HR-pQCT scans of the radius and tibia. Convergence, speedup, memory, and batch performance tests were completed using CPUs and GPUs on three laboratory-based systems with Windows, Linux, and Mac operating systems. ResultsAlthough varying by system and model size, time-per-iteration was as low as 0.03 s when an HR-pQCT-based radius model (6.45 million DOF) was solved with 3 GPUs. Strong scaling was achieved with GPU and CPU parallel processing, with strong parallel efficiencies when models were solved using 3 GPUs or ≤ 10 CPU threads. Errors in force, strain energy density, and Von Mises stress were as low as 0.1% when a convergence tolerance of 10−3 or smaller was used. ConclusionThe results of this study indicate that to maximize computational efficiency and minimize model solution times using FAIM software under the standardized tested conditions using µCT, XCT1 and XCT2 HR-pQCT image data, convergence tolerance set to 10−4, and 10 threads or 2 GPUs are sufficient for efficient solution times. Less strict convergence tolerances will improve solution times but will introduce more error in the outcome measures.

Multithreaded wedge detection method on triangular 3D CAD objects using mesh traversal method

In this study, a multithreaded method for triangular mesh three-dimensional computer aided design objects is proposed to detect and extract wedges. Wedge detection is time consuming process for such objects that have large number of facets. To take the advantage of parallel computing opportunities, the algorithm is refactored in this study. Scope of variables, memory management and stack use are optimized for efficient use of computational resources. The proposed method is focused to calculation efficiency and performance on multicore / multithreaded processors and it is evaluated with benchmark, complex and realistic objects.

Aggregation of multidimensional data for the decision support process for the management of microgrids with renewable energy sources

The object of research is the process of processing and storing data when making decisions on managing the life cycle of electricity generation and consumption in microgrids with renewable energy sources. The prospects of the study are due to the fact that in order to provide a full-fledged decision support process in the management of microgrids with renewable energy sources, it is necessary to consolidate and manipulate multidimensional data in multithreading and online information processing. To solve the problem, the theoretical methods of analysis, abstraction, induction and deduction were used. To ensure multidimensionality and multithreading of data processing, it is proposed to develop a data warehouse based on the snowflake data model. Efficiency of information processing in real time is provided by an operational database built on the principle of OLTP. The organization of the joint work of the data warehouse with the operational database, the consolidation and manipulation of data is provided by triggers. The result of the work is a data warehouse that will be used in the decision support system for managing energy microgrids, which will improve the efficiency of data processing and storage. This is achieved by combining the work of a centralized data warehouse with an operational database, as well as the use of a separate data mart for each user of the system. The practical significance of the work lies in the fact that the data warehouse will become part of the decision support system for processing information about the life cycle of energy in the management of energy infrastructure. Compared to using a single database for a decision support system, this approach ensures the speed of working with data and allows differentiating between the use of a data warehouse for analytics and data manipulation operations. The data warehouse was deployed in a cloud environment on the Amazon Web Services (AWS) platform and the Amazon Relational Database Service (Amazon RDS) web service. Secure access to client data is implemented using data marts.

A Common Tracking Software Project

The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

Vision-based aerial image mosaicking algorithm with object detection

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input Image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are Applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

Research on Pose Estimation Method of Medicine Box Based on Secondary Key Point Matching

Aiming at the difficulty and low efficiency of pose estimation in the process of automatic drug picking, a pose estimation method combining plane segmentation and key point matching is proposed. First, a point cloud hierarchical clustering algorithm is used to segment the plane area, and the improved secondary key point matching method is used to identify and locate the pill box in each thread, which is convenient for multi-thread processing. Then the point cloud plane normal vector estimation method based on least squares is used to realize the posture reconstruction of the pill box. 600 images with viewpoint change, rotation transformation and scale change were collected for testing, the experimental results show that the pose estimation speed of the medicine box can reach 11 frames/s, the positioning deviation is less than 5 mm, and the pose estimation deviation is within 2°. So the method is efficient and can accurately identify the pose of the medicine box.

Embedded Microprocessor Extension Design and Optimization for Real-Time Edge Computing

With the development of 5G communication technology, more and more applications could be integrated into one system. Edge computing system and mixed-criticality system may integrate tasks of different criticality levels, which brings better balance in isolation and performance. Such advantages make it gradually become a research hotspot in edge computing and real-time systems with 5G. The important content of designing a mixed-criticality system is how to reduce interference between tasks and how to schedule tasks efficiently to ensure that tasks of different criticality levels can meet time constraints. Instruction extension and hardware software cooperative support may be an effective solution. Based on a fine-grained multithreaded RISC-V processor, this article gives some extensions for real-time operations and proposes a hardware software cooperative real-time scheduling mechanism. Experimental results show that, compared with FlexPRET hardware, the performance of thread scheduling is improved by 22.94% on average. Compared with software scheduling, the performance of scheduling same programs and multiple programs are improved by 15.46% and 26.00%, respectively.

Eş zamanlı çok işlem parçacıklı işlemcilerde adalet için güvenli önbellek paylaşımı

Bulut bilişimin yaygın kullanımı, sanallaştırma ve uzaktan çalıştırma nedeniyle sunucu kaynaklarının birden fazla bilgisayar tarafından paylaşılması, donanımı paylaşılan bir kaynak haline getirmiş,ve donanım güvenliği daha fazla önem kazanmaya başlamıştır. Meltdown ve Spectre saldırılarında olduğu gibi önbellek tabanlı yan kanal saldırıları yoluyla şifreleme anahtarları gibi gizli bilgileri çıkarmak mümkündür. Güvenli donanım çalışmaları içinde, güvenli önbellek mimarileri üzerine yapılan çalışmalar daha derin bir odak noktası haline geldi. Güvenli önbellek mimarileri kaçınılmaz bir performans kaybıyla birlikte gelir, çünkü her zaman saldırganlardan bilgi gizlemenin bir işleme ya da saklama maliyeti vardır. Performansın düşüşünü kullanıcılar arasında adil bir şekilde paylaştırmak, güvenlik çözümlerinde göz ardı edilmiştir. Bu çalışma ile birlikte, mevcut mimarilerin performans bozulmalarını analiz edip, genel çerçevenin adaletliliğini artıran yeni bir çözüm sunuyoruz. Önbellek tabanlı yan kanal saldırılarına karşı, bir işlemci içindeki rakip iş parçacıkları arasında adalete saygı duyan güvenli bir önbellek mekanizması öneriyoruz. FairSDP mimarisini 4 iş parçacıklı ve 8 iş parçacıklı işlemcilerde değerlendirdik. Sonuç olarak, 4 iş parçacıklı bir sistemde ortalama olarak SecDCP (Güvenli ve Dinamik Önbellek Bölümleme) mimarisine kıyasla yüzde 38,6 performans artışı elde ediyoruz. 8 iş parçacıklı bir sistemde taban çizgisine göre yüzde 8,7'ye kadar performans iyileştirmesi, statik bölümlemeye kıyasla yüzde 9,2 daha iyi performans ve ortalama olarak SecDCP'ye göre yüzde 14,1 performans artışı sağlayabildiğimizi gösteriyoruz. Ayrıca, güvenli olmayan bir dinamik önbellek bölümleme tekniğine kıyasla adalet metriği açısından hemen hemen aynı sonuçları elde ederiz.

Mitigating execution unit contention in parallel applications using instruction‐aware mapping

SummaryParallel applications running on simultaneous multithreading (SMT) processors naturally compete for execution units when their threads are mapped to the same core. This issue is further aggravated when such threads execute similar instructions that stress the same execution unit type, making their execution to behave very similarly as if the threads were running sequentially. This, in turn, will lead to performance degradation and underutilization of hardware resources. This work proposes a completely transparent framework (no modifications to the source code are necessary) that automatically maps threads of multiple parallel applications on SMT processors. The framework focuses on improving performance by mitigating the contention on execution units, considering each thread's instruction types, which are detected at runtime by our framework. Results show performance gains of 21% (geometric mean), compared to the native scheduler of the operating system.

Software complex for multithreaded processing of scientific information for user services of Belarus Agricultural Library

The article is devoted to the implementation of the "Software complex for multithreaded processing of scientific information for user services of Belarus Agricultural Library ". The new system allows to keep records of users, store and correct their requests, send selected information in the form of a bibliographic list, send completed full texts of documents, monitor the required information in some resources in a semi-automatic mode, etc. The software package consists of user and administrative blocks. The administrative block includes several main modules: accounts (users, specialists, organizations), requests (thematic, for delivery, deliveries), monitoring (sources of bibliographic descriptions, monitoring of bibliographic descriptions) and statistics (organization / users, quantitative, specialist / requests, organization / inquiries, supplies). The introduction of this system will be the largest update of the remote user services of Belarus Agricultural Library over the past ten years.

The value chain in the digital age

The fourth industrial revolution causes changes in the organization and functioning of many enterprises. Digital transformation is a multi-threaded process covering practically all areas of life and economy. Digital transformation means implementation information technologies in all areas of business functioning. Businesses, both large and small, must adapt to the standards of digital “reality”. Digitalization is the driving force that will shape the industry of the future. The potential of digitization can be seen throughout the value chain, from product design, manufacturing and business processes, a fully digitized customer path, to new customer acquisition and customer service, to supply automation. The implementation of digital technologies of Industry 4.0 has a huge impact on the entire value chain, streamlining production and processes, improving management and quality of products and services, optimizing relationships between customers and organizations, bringing new business development opportunities and many benefits. The value chain in the era of digitization is becoming an important element generating noticeable profits. The aim of the study is to present the value chain in the era of digitization in the context of Industry 4.0, including the identification of its most key elements, including the formulation of strategies based on digital resources that bring added value, allowing to compete in the market and at the same time set new standards and business models, and to show the significant benefits of digitization of the value chain.

Qibo: a framework for quantum simulation with hardware acceleration

We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading Central Processing Unit (CPU), single Graphics Processing Unit (GPU) and multi-GPU devices.

MFDFA: Efficient multifractal detrended fluctuation analysis in python

Multifractal detrended fluctuation analysis (MFDFA) has become a central method to characterise the variability and uncertainty in empiric time series. Extracting the fluctuations on different temporal scales allows quantifying the strength and correlations in the underlying stochastic properties, their scaling behaviour, as well as the level of fractality. Several extensions to the fundamental method have been developed over the years, vastly enhancing the applicability of MFDFA, e.g. empirical mode decomposition for the study of long-range correlations and persistence. In this article we introduce an efficient, easy-to-use python library for MFDFA, incorporating the most common extensions and harnessing the most of multi-threaded processing for very fast calculations.

Adaptive Visual Servoing for Obstacle Avoidance of Micro Unmanned Aerial Vehicle with Optical Flow and Switched System Model

A vision-based adaptive switching controller that uses optical flow information to avoid obstacles for micro unmanned aerial vehicles (MUAV) is proposed in this paper. To use the optical flow to indicate the distance between the MUAV and the environment, we propose an algorithm with multi-thread processing such that the optical flow information is obtained reliably and continuously in the entire camera field of view. The flying behavior of considered MUAV is regarded as a switching system when considering different flying modes during the mission of obstacle avoidance. By the required flight direction for obstacle avoidance specified by the detected optical flow, an adaptive control scheme is designed to track the required trajectory in switching modes. The simulation result shows the tracking performances of the adaptive control with the switching system. The experiment of the whole system is completed to verify the obstacle avoidance capability of our system.

Advances in parallel and distributed computing and its applications

Advances in parallel and distributed computing and its applications