Download or read book High Performance Accelerator Modeling written by Frederick Cropp and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-ray free electron lasers and ultrafast electron diffraction (UED). To these ends, this thesis details efforts to use high-performance models --- high-fidelity models that execute quickly --- for controls and diagnostics. To demonstrate the generality of these techniques, this thesis focuses on three of the most successful photoinjector designs currently used worldwide: the UCLA/SLAC/BNL-type high-gradient S-band gun, the continuous-wave high-repetition rate VHF APEX gun and the L-band DESY-PITZ-type gun. Work will be shown from Pegasus (UCLA), HiRES (LBNL) and FAST (FNAL). Specifically, data-driven models for online virtual diagnostics are presented, in this case, in the context of UED at HiRES, leading to a temporal resolution improvement. Methods for improving the fidelity of physics-based models are discussed, with examples at HiRES, Pegasus and FAST. Markov-chain Monte Carlo analysis is applied to match simulations, in the context of photocathode studies at HiRES and Pegasus. Lastly, the augmentation of online model-based predictions with model-independent optimization is explored in a fluctuating environment at Pegasus and HiRES. A central theme of this dissertation is working with parameter fluctuations when modeling an accelerator beamline. Long-term drifts and shot-to-shot jitter exist in every accelerator to a varying degree and therefore play an important role in every chapter of this dissertation. This thesis attempts to address the issues associated with these fluctuations when trying to develop faithful model representations of the system.

Download or read book High Performance Computing Modeling Advances Accelerator Science for High Energy Physics written by and published by . This book was released on 2014 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development and optimization of particle accelerators are essential for advancing our understanding of the properties of matter, energy, space and time. Particle accelerators are complex devices whose behavior involves many physical effects on multiple scales. Therefore, advanced computational tools utilizing high-performance computing (HPC) are essential for accurately modeling them. In the past decade, the DOE SciDAC program has produced such accelerator-modeling tools, which have beem employed to tackle some of the most difficult accelerator science problems. In this article we discuss the Synergia beam-dynamics framework and its applications to high-intensity particle accelerator physics. Synergia is an accelerator simulation package capable of handling the entire spectrum of beam dynamics simulations. We present the design principles, key physical and numerical models in Synergia and its performance on HPC platforms. Finally, we present the results of Synergia applications for the Fermilab proton source upgrade, known as the Proton Improvement Plan (PIP).

Download or read book Advanced Modeling of High Intensity Accelerators written by and published by . This book was released on 1998 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The goals of this project were three-fold: (1) to develop a new capability, based on high performance (parallet to) computers, to perform large scale simulations of high intensity accelerators; (2) to apply this capability to modeling high intensity accelerators under design at LANL; and (3) to use this new capability to improve the understanding of the physics of intense charge particle beams, especially in regard to the issue of beam halo formation. All of these goals were met. In particular, the authors introduced split-operator methods as a powerful and efficient means to simulate intense beams in the presence of rapidly varying accelerating and focusing fields. They then applied these methods to develop scaleable, parallel beam dynamics codes for modeling intense beams in linacs, and in the process they implemented a new three-dimensional space charge algorithm. They also used the codes to study a number of beam dynamics issues related to the Accelerator Production of Tritium (APT) project, and in the process performed the largest simulations to date for any accelerator design project. Finally, they used the new modeling capability to provide direction and validation to beam physics studies, helping to identify beam mismatch as a major source of halo formation in high intensity accelerators. This LDRD project ultimately benefited not only LANL but also the US accelerator community since, by promoting expertise in high performance computing and advancing the state-of-the-art in accelerator simulation, its accomplishments helped lead to approval of a new DOE Grand Challenge in Computational Accelerator Physics.

Download or read book High Performance Computing Systems Performance Modeling Benchmarking and Simulation written by Stephen A. Jarvis and published by Springer. This book was released on 2015-04-20 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 5th International Workshop, PMBS 2014 in New Orleans, LA, USA in November 2014. The 12 full and 2 short papers presented in this volume were carefully reviewed and selected from 53 submissions. The papers cover topics on performance benchmarking and optimization; performance analysis and prediction; and power, energy and checkpointing.

Download or read book Beam based Correction and Optimization for Accelerators written by Xiaobiao Huang and published by CRC Press. This book was released on 2019-12-05 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides systematic coverage of the beam-based techniques that accelerator physicists use to improve the performance of large particle accelerators, including synchrotrons and linacs. It begins by discussing the basic principles of accelerators, before exploring the various error sources in accelerators and their impact on the machine's performances. The book then demonstrates the latest developments of beam-based correction techniques that can be used to address such errors and covers the new and expanding area of beam-based optimization. This book is an ideal, accessible reference book for physicists working on accelerator design and operation, and for postgraduate studying accelerator physics. Features: Entirely self-contained, exploring the theoretic background, including algorithm descriptions, and providing application guidance Accompanied by source codes of the main algorithms and sample codes online Uses real-life accelerator problems to illustrate principles, enabling readers to apply techniques to their own problems Xiaobiao Huang is an accelerator physicist at the SLAC National Accelerator Laboratory at Stanford University, USA. He graduated from Tsinghua University with a Bachelor of Science in Physics and a Bachelor of Engineering in Computer Science in 1999. He earned a PhD in Accelerator Physics from Indiana University, Bloomington, Indiana, USA, in 2005. He spent three years on thesis research work at Fermi National Accelerator Laboratory from 2003-2005. He has worked at SLAC as a staff scientist since 2006. He became Accelerator Physics Group Leader of the SPEAR3 Division, Accelerator Directorate in 2015. His research work in accelerator physics ranges from beam dynamics, accelerator design, and accelerator modelling and simulation to beam based measurements, accelerator control, and accelerator optimization. He has taught several courses at US Particle Accelerator School (USPAS), including Beam Based Diagnostics, Accelerator Physics, Advanced Accelerator Physics, and Special Topics in Accelerator Physics.

Download or read book High Performance Computing in Accelerating Structure Design And Analysis written by L. Q. Lee and published by . This book was released on 2006 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Future high-energy accelerators such as the Next Linear Collider (NLC) will accelerate multi-bunch beams of high current and low emittance to obtain high luminosity, which put stringent requirements on the accelerating structures for efficiency and beam stability. While numerical modeling has been quite standard in accelerator R & D, designing the NLC accelerating structure required a new simulation capability because of the geometric complexity and level of accuracy involved. Under the US DOE Advanced Computing initiatives (first the Grand Challenge and now SciDAC), SLAC has developed a suite of electromagnetic codes based on unstructured grids and utilizing high performance computing to provide an advanced tool for modeling structures at accuracies and scales previously not possible. This paper will discuss the code development and computational science research (e.g. domain decomposition, scalable eigensolvers, adaptive mesh refinement) that have enabled the large-scale simulations needed for meeting the computational challenges posed by the NLC as well as projects such as the PEP-II and RIA. Numerical results will be presented to show how high performance computing has made a qualitative improvement in accelerator structure modeling for these accelerators, either at the component level (single cell optimization), or on the scale of an entire structure (beam heating and long range wakefields).

Download or read book Algorithm accelerator Co design for High performance and Secure Deep Learning written by Weizhe Hua and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has emerged as a new engine for many of today's artificial intelligence/machine learning systems, leading to several recent breakthroughs in vision and natural language processing tasks.However, as we move into the era of deep learning with billions and even trillions of parameters, meeting the computational and memory requirements to train and serve state-of-the-art models has become extremely challenging. Optimizing the computational cost and memory footprint of deep learning models for better system performance is critical to the widespread deployment of deep learning. Moreover, a massive amount of sensitive and private user data is exposed to the deep learning system during the training or serving process. Therefore, it is essential to investigate potential vulnerabilities in existing deep learning hardware, and then design secure deep learning systems that provide strong privacy guarantees for user data and the models that learn from the data. In this dissertation, we propose to co-design the deep learning algorithms and hardware architectural techniques to improve both the performance and security/privacy of deep learning systems. On high-performance deep learning, we first introduce channel gating neural network (CGNet), which exploits the dynamic sparsity of specific inputs to reduce computation of convolutional neural networks. We also co-develop an ASIC accelerator for CGNet that can turn theoretical FLOP reduction into wall-clock speedup. Secondly, we present Fast Linear Attention with a Single Head (FLASH), a state-of-the-art language model specifically designed for Google's TPU that can achieve transformer-level quality with linear complexity with respect to the sequence length. Through our empirical studies on masked language modeling, auto-regressive language modeling, and fine-tuning for question answering, FLASH achieves at least similar if not better quality compared to the augmented transformer, while being significantly faster (e.g., up to 12 times faster). On the security of deep learning, we study the side-channel vulnerabilities of existing deep learning accelerators. We then introduce a secure accelerator architecture for privacy-preserving deep learning, named GuardNN. GuardNN provides a trusted execution environment (TEE) with specialized protection for deep learning, and achieves a small trusted computing base and low protection overhead at the same time. The FPGA prototype of GuardNN achieves a maximum performance overhead of 2.4\% across four different modern DNNs models for ImageNet.

Download or read book Community Petascale Project for Accelerator Science and Simulation written by and published by . This book was released on 2011 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design and performance optimization of particle accelerators are essential for the success of the DOE scientific program in the next decade. Particle accelerators are very complex systems whose accurate description involves a large number of degrees of freedom and requires the inclusion of many physics processes. Building on the success of the SciDAC-1 Accelerator Science and Technology project, the SciDAC-2 Community Petascale Project for Accelerator Science and Simulation (ComPASS) is developing a comprehensive set of interoperable components for beam dynamics, electromagnetics, electron cooling, and laser/plasma acceleration modelling. ComPASS is providing accelerator scientists the tools required to enable the necessary accelerator simulation paradigm shift from high-fidelity single physics process modeling (covered under SciDAC1) to high-fidelity multiphysics modeling. Our computational frameworks have been used to model the behavior of a large number of accelerators and accelerator R & D experiments, assisting both their design and performance optimization. As parallel computational applications, the ComPASS codes have been shown to make effective use of thousands of processors. ComPASS is in the first year of executing its plan to develop the next-generation HPC accelerator modeling tools. ComPASS aims to develop an integrated simulation environment that will utilize existing and new accelerator physics modules with petascale capabilities, by employing modern computing and solver technologies. The ComPASS vision is to deliver to accelerator scientists a virtual accelerator and virtual prototyping modeling environment, with the necessary multiphysics, multiscale capabilities. The plan for this development includes delivering accelerator modeling applications appropriate for each stage of the ComPASS software evolution. Such applications are already being used to address challenging problems in accelerator design and optimization. The ComPASS organization for software development and applications accounts for the natural domain areas (beam dynamics, electromagnetics, and advanced acceleration), and all areas depend on the enabling technologies activities, such as solvers and component technology, to deliver the desired performance and integrated simulation environment. The ComPASS applications focus on computationally challenging problems important for design or performance optimization to all major HEP, NP, and BES accelerator facilities. With the cost and complexity of particle accelerators rising, the use of computation to optimize their designs and find improved operating regimes becomes essential, potentially leading to significant cost savings with modest investment.

Download or read book Beam Dynamics In High Energy Particle Accelerators written by Andrzej Wolski and published by World Scientific. This book was released on 2014-01-21 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particle accelerators are essential tools for scientific research in fields as diverse as high energy physics, materials science and structural biology. They are also widely used in industry and medicine. Producing the optimum design and achieving the best performance for an accelerator depends on a detailed understanding of many (often complex and sometimes subtle) effects that determine the properties and behavior of the particle beam. Beam Dynamics in High Energy Particle Accelerators provides an introduction to the concepts underlying accelerator beam line design and analysis, taking an approach that emphasizes the elegance of the subject and leads into the development of a range of powerful techniques for understanding and modeling charged particle beams.

Download or read book Performance Modeling and Anlysis in High performance Reconfigurable Computing written by Tobias Schumacher and published by . This book was released on 2011 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reconfigurable Computing has received a high level of attention during the last years. Scientists presented accelerators for different algorithm classes gaining speedups of several orders of magnitude. Major supercomputer vendors came out with high-performance computers that tightly connect reconfigurable devices to the CPUs and/or to the memory subsystem. One of the major focuses of recent research is put on the programmability of these reconfigurable high-performance computers. Despite great research results in this topic, there are still several challenges which make the development process of reconfigurable accelerators a time consuming and error-prone process. This thesis introduces a novel modeling technique which supports algorithms targeted at heterogeneous systems that provide commodity CPUs as well as reconfigurable accelerators. In contrast to existing modeling approaches, the method presented is not based on a static model of the target architecture, but allows for specifying the architecture model along with the execution model of the algorithms to be implemented. A key aspect considered hereby is the time needed for data transfers. The modeling approach is supported by the IMORC architectural template which eases the implementation of the modeled accelerator. Additionally, the thesis introduces methods for analyzing the performance of the developed architecture during run time and for optimizing the models with the help of the data obtained. The approach is finally evaluated by demonstrating several case studies. ; eng

Download or read book Accelerator Programming Using Directives written by Sunita Chandrasekaran and published by Springer. This book was released on 2018-02-09 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 4th International Workshop on Accelerator Programming Using Directives, WACCPD 2017, held in Denver, CO, USA, in November 2017. The 9 full papers presented have been carefully reviewed and selected from 14 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: applications; environments; and program evaluation.

Download or read book Simulating Dataflow Accelerators for Deep Learning Application in Heterogeneous System written by Quang Anh Hoang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the past few decades, deep learning has emerged as an essential discipline that broadens the horizon of the knowledge of humankind. At its core, Deep Neural Networks (DNN) play a vital role in processing input data to generate predictions or decisions (inference step), with their accuracy ameliorated by extensive training (training step). As the complexity of the problem increases, the number of layers in DNN models tends to rise. Such complex models require more computations and take longer to produce an output. Additionally, the large number of calculations require a tremendous amount of power. Therefore, improving energy efficiency is a primary design consideration. To address this concern, researchers have studied domain-specific architecture to develop highly efficient hardware tailored for a given application, which performs a given set of computations at a lower energy cost. An energy-efficient yet high-performance system is created by pairing this application-specific accelerator with a General-Purpose Processor (GPP). This heterogeneity helps offload the heavy computations to the accelerator while handling less computation intensive tasks on the GPP. In this thesis, we study the performance of dataflow accelerators integrated into a heterogeneous architecture for executing deep learning workloads. Fundamental to these accelerators is their high levels of concurrency in executing computations simultaneously, making them suitable to exploit data parallelism present in DNN operations. With the limited bandwidth of interconnection between accelerator and main memory being one of the critical constraints of a heterogeneous system, a tradeoff between memory overhead and computational runtime is worth considering. This tradeoff is the main criteria we use in this thesis to evaluate the performance of each architecture and configuration. A model of dataflow memristive crossbar array accelerator is first proposed to expand the scope of the heterogeneous simulation framework towards architectures with analog and mixed-signal circuits. At the core of this accelerator, an array of resistive memory cells connected in crossbar architecture is used for computing matrix multiplications. This design aims to study the effect of memory-performance tradeoffs on systems with analog components. Therefore, a comparison between memristive crossbar array architecture and its digital counterpart, systolic array, is presented. While existing studies focus on heterogeneous systems with digital components, this approach is the first to consider a mixed-signal accelerator incorporated with a general-purpose processor for deep learning workloads. Finally, an application interface software is designed to configure the system's architecture and map DNN layers to simulated hardware. At the core of this software is a DNN model parser-partitioner, which provides subsequent tasks of generating a hardware configuration for the accelerator and assigns partitioned workload to the simulated accelerator. The interface provided by this software can be developed further to incorporate scheduling and mapping algorithms. This extension will produce a synthesizer that will facilitate the following: • Hardware configuration: generate the optimal configuration of system hardware, incorporating the key hardware characteristics such as the number of accelerators, dimension of processing array, and memory allocation for each accelerator. • Schedule of execution: implement a mapping algorithm to decide on an efficient distribution and schedule of partitioned workloads. For future development, this synthesizer will unite the first two stages in system's design flow. In the first analysis stage, simulators search for optimal design aspects under a short time frame based on abstract application graphs and the system's specifications. In architecture stage, within the optimal design region from previous stage, simulators refine their findings by studying further details on architectural level. This inter-stage fusion, once finished, can bring the high accuracy of architectural-level simulation tool closer to analysis stage. In the opposite direction, mapping algorithms implemented in analysis tools can provide architectural exploration with near-optimal scheduling. Together, this stack of software can significantly reduce the time searching for specifications with optimal efficiency.

Download or read book US DOE Grand Challenge in Computational Accelerator Physics written by and published by . This book was released on 1998 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particle accelerators are playing an increasingly important role in basic and applied science, and are enabling new accelerator-driven technologies. But the design of next-generation accelerators, such as linear colliders and high intensity linacs, will require a major advance in numerical modeling capability due to extremely stringent beam control and beam loss requirements, and the presence of highly complex three-dimensional accelerator components. To address this situation, the U.S. Department of Energy has approved a ''Grand Challenge'' in Computational Accelerator Physics, whose primary goal is to develop a parallel modeling capability that will enable high performance, large scale simulations for the design, optimization, and numerical validation of next-generation accelerators. In this paper we report on the status of the Grand Challenge.

Download or read book Commnity Petascale Project for Accelerator Science And Simulation written by and published by . This book was released on 2011 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design and performance optimization of particle accelerators are essential for the success of the DOE scientific program in the next decade. Particle accelerators are very complex systems whose accurate description involves a large number of degrees of freedom and requires the inclusion of many physics processes. Building on the success of the SciDAC-1 Accelerator Science and Technology project, the SciDAC-2 Community Petascale Project for Accelerator Science and Simulation (ComPASS) is developing a comprehensive set of interoperable components for beam dynamics, electromagnetics, electron cooling, and laser/plasma acceleration modelling. ComPASS is providing accelerator scientists the tools required to enable the necessary accelerator simulation paradigm shift from high-fidelity single physics process modeling (covered under SciDAC1) to high-fidelity multiphysics modeling. Our computational frameworks have been used to model the behavior of a large number of accelerators and accelerator R & D experiments, assisting both their design and performance optimization. As parallel computational applications, the ComPASS codes have been shown to make effective use of thousands of processors.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Tools for High Performance Computing 2014 written by Christoph Niethammer and published by Springer. This book was released on 2015-06-02 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical simulation and modelling using High Performance Computing has evolved into an established technique in academic and industrial research. At the same time, the High Performance Computing infrastructure is becoming ever more complex. For instance, most of the current top systems around the world use thousands of nodes in which classical CPUs are combined with accelerator cards in order to enhance their compute power and energy efficiency. This complexity can only be mastered with adequate development and optimization tools. Key topics addressed by these tools include parallelization on heterogeneous systems, performance optimization for CPUs and accelerators, debugging of increasingly complex scientific applications and optimization of energy usage in the spirit of green IT. This book represents the proceedings of the 8th International Parallel Tools Workshop, held October 1-2, 2014 in Stuttgart, Germany – which is a forum to discuss the latest advancements in the parallel tools.

Download or read book Data Orchestration in Deep Learning Accelerators written by Tushar Krishna and published by Springer Nature. This book was released on 2022-05-31 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.