Plasma Physics Computations on Emerging Hardware Architectures

This thesis explores the potential of emerging hardware architectures to increase the impact of high performance computing in fusion plasma physics research. For next generation tokamaks like ITER, realistic simulations and data-processing tasks will become significantly more demanding of computational resources than current facilities. It is therefore essential to investigate how emerging hardware such as the graphics processing unit (GPU) and field-programmable gate array (FPGA) can provide the required computing power for large data-processing tasks and large scale simulations in plasma physics specific computations. The use of emerging technology is investigated in three areas relevant to nuclear fusion: (i) a GPU is used to process the large amount of raw data produced by the synthetic aperture microwave imaging (SAMI) plasma diagnostic, (ii) the use of a GPU to accelerate the solution of the Bateman equations which model the evolution of nuclide number densities when subjected to neutron irradiation in tokamaks, and (iii) an FPGA-based dataflow engine is applied to compute massive matrix multiplications, a feature of many computational problems in fusion and more generally in scientific computing. The GPU data processing code for SAMI provides a 60x acceleration over the previous IDL-based code, enabling inter-shot analysis in future campaigns and the data-mining (and therefore analysis) of stored raw data from previous MAST campaigns. The feasibility of porting the whole Bateman solver to a GPU system is demonstrated and verified against the industry standard FISPACT code. Finally a dataflow approach to matrix multiplication is shown to provide a substantial acceleration compared to CPU-based approaches and, whilst not performing as well as a GPU for this particular problem, is shown to be much more energy efficient. Emerging hardware technologies will no doubt continue to provide a positive contribution in terms of performance to many areas of fusion research and several exciting new developments are on the horizon with tighter integration of GPUs and FPGAs with their host central processor units. This should not only improve performance and reduce data transfer bottlenecks, but also allow more user-friendly programming tools to be developed. All of this has implications for ITER and beyond where emerging hardware technologies will no doubt provide the key to delivering the computing power required to handle the large amounts of data and more realistic simulations demanded by these complex systems

Molecular Dynamics Simulation of Iron — A Review

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Advanced photonic and electronic systems WILGA 2018

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers around 400 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2018 was the XLII edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2018 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445. WILGA 2018 works were published in Proc. SPIE vol.10808

Domain Specific Computing in Tightly-Coupled Heterogeneous Systems

Over the past several decades, researchers and programmers across many disciplines have relied on Moores law and Dennard scaling for increases in compute capability in modern processors. However, recent data suggest that the number of transistors per square inch on integrated circuits is losing pace with Moores laws projection due to the breakdown of Dennard scaling at smaller semiconductor process nodes. This has signaled the beginning of a new “golden age in computer architecture” in which the paradigm will be shifted from improving traditional processor performance for general tasks to architecting hardware that executes a class of applications in a high-performing manner. This shift will be paved, in part, by making compute systems more heterogeneous and investigating domain specific architectures. However, the notion of domain specific architectures raises many research questions. Specifically, what constitutes a domain? How does one architect hardware for a specific domain? In this dissertation, we present our work towards domain specific computing. We start by constructing a guiding definition for our target domain and then creating a benchmark suite of applications based on our domain definition. We then use quantitative metrics from the literature to characterize our domain in order to gain insights regarding what would be most beneficial in hardware targeted specifically for the domain. From the characterization, we learn that data movement is a particularly salient aspect of our domain. Motivated by this fact, we evaluate our target platform, the Intel HARPv2 CPU+FPGA system, for architecting domain specific hardware through a portability and performance evaluation. To guide the creation of domain specific hardware for this platform, we create a novel tool to quantify spatial and temporal locality. We apply this tool to our benchmark suite and use the generated outputs as features to an unsupervised clustering algorithm. We posit that the resulting clusters represent sub-domains within our originally specified domain; specifically, these clusters inform whether a kernel of computation should be designed as a widely vectorized or deeply pipelined compute unit. Using the lessons learned from the domain characterization and hardware platform evaluation, we outline our process of designing hardware for our domain, and empirically verify that our prediction regarding a wide or deep kernel implementation is correct

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

A Three Layered Framework for Annual Indoor Airflow CFD Simulation

Computational fluid dynamics (CFD) is one of the branches of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems that involve fluid flows. Computers are used to perform the millions of calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions. Indoor airflow simulations are necessary for building emergency management, preliminary design of sustainable buildings, and real-time indoor environment control. The simulation should also be informative since the airflow motion, temperature distribution, and contaminant concentration is important. However, CFD computation is usually time-consuming, and not suitable for simulating real-time indoor air movement. Many researchers are concentrating on both hardware utilization and CFD algorithms, to make simulation much faster. Fast flow simulations are important for some applications in the building industry, such as the conceptual design of indoor environment, or they are coupled with energy simulation to provide deep analysis on the performance of the buildings. Such application does not require the same high level of accuracy as traditional CFD simulation because it only requires conceptual or semi-accurate distributions of the flow but within a short computing time. However, year round simulation is needed rather than the analysis of two or three extreme cases in order to help the designer investigate the problem clearly. To meet these special needs, an efficient and informative fluid simulation method is needed to provide fast airflow simulation with an inevitable but nominal compromise in accuracy. This research provides a comprehensive workflow for the designer to simulate and analyze the annual indoor environment. In addition to the hardware acceleration deployed, fast fluid simulation algorithm is developed, and a machine learning based interpolation is used to allow the simulation coverage to be conducted annually. The outcome of this research is a methodology that allows the annual simulation time similar to the one used to perform two or three extreme cases of simulation using current methods