288 research outputs found

    Analysing and Reducing Costs of Deep Learning Compiler Auto-tuning

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    Deep Learning (DL) is significantly impacting many industries, including automotive, retail and medicine, enabling autonomous driving, recommender systems and genomics modelling, amongst other applications. At the same time, demand for complex and fast DL models is continually growing. The most capable models tend to exhibit highest operational costs, primarily due to their large computational resource footprint and inefficient utilisation of computational resources employed by DL systems. In an attempt to tackle these problems, DL compilers and auto-tuners emerged, automating the traditionally manual task of DL model performance optimisation. While auto-tuning improves model inference speed, it is a costly process, which limits its wider adoption within DL deployment pipelines. The high operational costs associated with DL auto-tuning have multiple causes. During operation, DL auto-tuners explore large search spaces consisting of billions of tensor programs, to propose potential candidates that improve DL model inference latency. Subsequently, DL auto-tuners measure candidate performance in isolation on the target-device, which constitutes the majority of auto-tuning compute-time. Suboptimal candidate proposals, combined with their serial measurement in an isolated target-device lead to prolonged optimisation time and reduced resource availability, ultimately reducing cost-efficiency of the process. In this thesis, we investigate the reasons behind prolonged DL auto-tuning and quantify their impact on the optimisation costs, revealing directions for improved DL auto-tuner design. Based on these insights, we propose two complementary systems: Trimmer and DOPpler. Trimmer improves tensor program search efficacy by filtering out poorly performing candidates, and controls end-to-end auto-tuning using cost objectives, monitoring optimisation cost. Simultaneously, DOPpler breaks long-held assumptions about the serial candidate measurements by successfully parallelising them intra-device, with minimal penalty to optimisation quality. Through extensive experimental evaluation of both systems, we demonstrate that they significantly improve cost-efficiency of autotuning (up to 50.5%) across a plethora of tensor operators, DL models, auto-tuners and target-devices

    Magnetic Material Modelling of Electrical Machines

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    The need for electromechanical energy conversion that takes place in electric motors, generators, and actuators is an important aspect associated with current development. The efficiency and effectiveness of the conversion process depends on both the design of the devices and the materials used in those devices. In this context, this book addresses important aspects of electrical machines, namely their materials, design, and optimization. It is essential for the design process of electrical machines to be carried out through extensive numerical field computations. Thus, the reprint also focuses on the accuracy of these computations, as well as the quality of the material models that are adopted. Another aspect of interest is the modeling of properties such as hysteresis, alternating and rotating losses and demagnetization. In addition, the characterization of materials and their dependence on mechanical quantities such as stresses and temperature are also considered. The reprint also addresses another aspect that needs to be considered for the development of the optimal global system in some applications, which is the case of drives that are associated with electrical machines

    Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

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    Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and scales applications. An iDSL includes domain-specific language constructs, a compilation toolchain, and a runtime providing task scheduling, data placement, and workload balancing across and within heterogeneous nodes. In this work, we focus on the runtime framework. We introduce a novel design and extension of a runtime framework, the Parallel Runtime Environment for Multicore Applications. In response to the ever-increasing intra/inter-node concurrency, the runtime system supports efficient task scheduling and workload balancing at both levels while allowing the development of custom policies. Moreover, the new framework provides abstractions supporting the utilization of heterogeneous distributed nodes consisting of CPUs and GPUs and is extensible to other devices. We demonstrate that by utilizing this work, an application (or the iDSL) can scale its performance on heterogeneous exascale-era supercomputers with minimal effort. A future goal for this framework (out of the scope of this thesis) is to be integrated with machine learning to improve its decision-making and performance further. As a bridge to this goal, since the framework is under development, we experiment with data from Nuclear Physics Particle Accelerators and demonstrate the significant improvements achieved by utilizing machine learning in the hit-based track reconstruction process

    MOCAST 2021

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    The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    FPGA-based range-limited molecular dynamics acceleration

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    Molecular Dynamics (MD) is a computer simulation technique that executes iteratively over discrete, infinitesimal time intervals. It has been a widely utilized application in the fields of material sciences and computer-aided drug design for many years, serving as a crucial benchmark in high-performance computing (HPC). Numerous MD packages have been developed and effectively accelerated using GPUs. However, as the limits of Moore's Law are reached, the performance of an individual computing node has reached its bottleneck, while the performance of multiple nodes is primarily hindered by scalability issues, particularly when dealing with small datasets. In this thesis, the acceleration with respect to small datasets is the main focus. With the recent COVID-19 pandemic, drug discovery has gained significant attention, and Molecular Dynamics (MD) has emerged as a crucial tool in this process. Particularly, in the critical domain of drug discovery, small simulations involving approximately ~50K particles are frequently employed. However, it is important to note that small simulations do not necessarily translate to faster results, as long-term simulations comprising billions of MD iterations and more are essential in this context. In addition to dataset size, the problem of interest is further constrained. Referred to as the most computationally demanding aspect of MD, the evaluation of range-limited (RL) forces not only accounts for 90% of the MD computation workload but also involves irregular mapping patterns of 3-D data onto 2-D processor networks. To emphasize, this thesis centers around the acceleration of RL MD specifically for small datasets. In order to address the single-node bottleneck and multi-node scaling challenges, the thesis is organized into two progressive stages of investigation. The first stage delves extensively into enhancing single-node efficiency by examining various factors such as workload mapping from 3-D to 2-D, data routing, and data locality. The second stage focuses on studying multi-node scalability, with a particular emphasis on strong scaling, bandwidth demands, and the synchronization mechanisms between nodes. Through our study, the results show our design on a Xilinx U280 FPGA achieves 51.72x and 4.17x speedups with respect to an Intel Xeon Gold 6226R CPU, and a Quadro RTX 8000 GPU. Our research towards strong scaling also demonstrates that 8 Xilinx U280 FPGAs connected to a switch achieves 4.67x speedup compared to an Nvidia V100 GP

    Recent Advances in Single-Particle Tracking: Experiment and Analysis

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    This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion

    Modern Applications in Optics and Photonics: From Sensing and Analytics to Communication

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    Optics and photonics are among the key technologies of the 21st century, and offer potential for novel applications in areas such as sensing and spectroscopy, analytics, monitoring, biomedical imaging/diagnostics, and optical communication technology. The high degree of control over light fields, together with the capabilities of modern processing and integration technology, enables new optical measurement systems with enhanced functionality and sensitivity. They are attractive for a range of applications that were previously inaccessible. This Special Issue aims to provide an overview of some of the most advanced application areas in optics and photonics and indicate the broad potential for the future

    Design and Code Optimization for Systems with Next-generation Racetrack Memories

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    With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market. Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM . This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation. Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators
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