Parallel machine architecture and compiler design facilities

The objective is to provide an integrated simulation environment for studying and evaluating various issues in designing parallel systems, including machine architectures, parallelizing compiler techniques, and parallel algorithms. The status of Delta project (which objective is to provide a facility to allow rapid prototyping of parallelized compilers that can target toward different machine architectures) is summarized. Included are the surveys of the program manipulation tools developed, the environmental software supporting Delta, and the compiler research projects in which Delta has played a role

Total recall in distributive associative memories

Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions

Solar Seismology from Space. a Conference at Snowmass, Colorado

The quality of the ground based observing environment suffers from several degrading factors: diurnal interruptions and thermal variations, atmospheric seeing and transparency fluctuations and adverse weather interruptions are among the chief difficulties. The limited fraction of the solar surface observable from only one vantage point is also a potential limitation to the quality of the data available without going to space. Primary conference goals were to discuss in depth the scientific return from current observations and analyses of solar oscillations, to discuss the instrumental and site requirements for realizing the full potential of the seismic analysis method, and to help bring new workers into the field by collecting and summarizing the key background theory. At the conclusion of the conference there was a clear consensus that ground based observation would not be able to provide data of the quality required to permit a substantial analysis of the solar convection zone dynamics or to permit a full deduction of the solar interior structure

Data Parallel C++

Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++

Probabilistic characterization and synthesis of complex driven systems

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 194-204).Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.by Bernd Schoner.Ph.D

Data Parallel C++

Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++

Topology Optimization of Irregular Shaped Pressure Vessels Using a Level-Set Method

Advances in manufacturing capabilities, such as additive manufacturing, have expanded the design freedom given to engineers enabling more efficient designs through the use of complex geometries. However, determining the optimal geometric structure for a given set of performance criteria can be quite challenging when given such design freedom. One technique to do so is with the use of topology optimization methods, in which optimal material distribution within a given design space is determined. Many established topology optimization methods are developed such that a set of boundary conditions are prescribed to the design domain and remain fixed throughout the optimization process of determining the material distribution. This eliminates the ability to implement design dependent loading conditions, such as pressure loading, which requires tracking (following) the pressure surface as the geometry evolves during the optimization process. In this thesis, a level-set topology optimization method is implemented based on voxel elements on design domains in R^3 subjected to internal pressure loading, such as in the case of a non-spherical or cylindrical pressure vessel. Following a thorough literature review, a level-set function was chosen to define a crisp material/void boundary for identifying loading conditions caused by the applied pressure. This pressure loading is calculated as an applied traction across all material elements, excluding exterior surface nodes. This results in an equal and opposite cancelation throughout the material domain and leaving forces only at desired nodes along the material/void boundary. This implementation only requires material elements to be meshed, allowing for remeshing throughout the process to increase accuracy while saving computational cost by excluding void regions. Additionally, to improve convergence, the Lagrangian formulation of a penalty is replaced by a method analogous to PID-control systems as the algorithm hones in on convergence. To test the effectiveness of the method and the practicality of designing an irregular pressure vessel, the gas storage tanks of the MK16 rebreather for the US NAVY were redesigned within the current system’s geometric constraints in an effort to increase gas storage capacity. To do this, an outside domain geometry of the irregular shaped pressure vessel was defined, and not subject to change, while the optimization code was executed on the interior structure to minimize compliance subjected to an overall volume fraction constraint. This was done at various target volume fractions, and then stresses and compliance values were analyzed and compared to the existing pressure vessel of the MK16. The findings of this research concluded that designing an irregular shaped pressure vessel is a viable means of increasing storage capacity although future work would need to be executed to manufacture and experimentally validate these findings

Optimization of Parallel Computations on Heterogeneous GPU-Based Systems

In this master thesis, we design and implement MultiStream: a solution that extends the existing data parallel skeleton library SkePU with NVIDIA CUDA Streams to overlap main memory – device memory data transfers with CUDA Kernel executions. We show the benefits of this approach using a task-parallel framework, FastFlow, on-top of SkePU. Finally, we compare the MultiStream extended SkePU to an ad-hoc solution to discuss the tradeoffs between the level of abstraction and the maximum achievable performance

Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription

This thesis presents techniques for the modelling of musical signals, with particular regard to monophonic and polyphonic pitch estimation. Musical signals are modelled as a set of notes, each comprising of a set of harmonically-related sinusoids. An hierarchical model is presented that is very general and applicable to any signal that can be decomposed as the sum of basis functions. Parameter estimation is posed within a Bayesian framework, allowing for the incorporation of prior information about model parameters. The resulting posterior distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters of several frames of data are estimated jointly to achieve a more robust detection. A general model for the description of time-varying homogeneous and heterogeneous multiple component signals is developed, and then applied to the analysis of musical signals. The importance of high level musical and perceptual psychological knowledge in the formulation of the model is highlighted, and attention is drawn to the limitation of pure signal processing techniques for dealing with musical signals. Gestalt psychological grouping principles motivate the hierarchical signal model, and component identifiability is considered in terms of perceptual streaming where each component establishes its own context. A major emphasis of this thesis is the practical application of MCMC techniques, which are generally deemed to be too slow for many applications. Through the design of efficient transition kernels highly optimised for harmonic models, and by careful choice of assumptions and approximations, implementations approaching the order of realtime are viable.Engineering and Physical Sciences Research Counci