Heterogeneous parallel virtual machine: A portable program representation and compiler for performance and energy optimizations on heterogeneous parallel systems

Programming heterogeneous parallel systems, such as the SoCs (System-on-Chip) on mobile and edge devices is extremely difficult; the diverse parallel hardware they contain exposes vastly different hardware instruction sets, parallelism models and memory systems. Moreover, a wide range of diverse hardware and software approximation techniques are available for applications targeting heterogeneous SoCs, further exacerbating the programmability challenges. In this thesis, we alleviate the programmability challenges of such systems using flexible compiler intermediate representation solutions, in order to benefit from the performance and superior energy efficiency of heterogeneous systems. First, we develop Heterogeneous Parallel Virtual Machine (HPVM), a parallel program representation for heterogeneous systems, designed to enable functional and performance portability across popular parallel hardware. HPVM is based on a hierarchical dataflow graph with side effects. HPVM successfully supports three important capabilities for programming heterogeneous systems: a compiler intermediate representation (IR), a virtual instruction set (ISA), and a basis for runtime scheduling. We use the HPVM representation to implement an HPVM prototype, defining the HPVM IR as an extension of the Low Level Virtual Machine (LLVM) IR. Our results show comparable performance with optimized OpenCL kernels for the target hardware from a single HPVM representation using translators from HPVM virtual ISA to native code, IR optimizations operating directly on the HPVM representation, and the capability for supporting flexible runtime scheduling schemes from a single HPVM representation. We extend HPVM to ApproxHPVM, introducing hardware-independent approximation metrics in the IR to enable maintaining accuracy information at the IR level and mapping of application-level end-to-end quality metrics to system level "knobs". The approximation metrics quantify the acceptable accuracy loss for individual computations. Application programmers only need to specify high-level, and end-to-end, quality metrics, instead of detailed parameters for individual approximation methods. The ApproxHPVM system then automatically tunes the accuracy requirements of individual computations and maps them to approximate hardware when possible. ApproxHPVM results show significant performance and energy improvements for popular deep learning benchmarks. Finally, we extend to ApproxHPVM to ApproxTuner, a compiler and runtime system for approximation. ApproxTuner extends ApproxHPVM with a wide range of hardware and software approximation techniques. It uses a three step approximation tuning strategy, a combination of development-time, install-time, and dynamic tuning. Our strategy ensures software portability, even though approximations have highly hardware-dependent performance, and enables efficient dynamic approximation tuning despite the expensive offline steps. ApproxTuner results show significant performance and energy improvements across 7 Deep Neural Networks and 3 image processing benchmarks, and ensures that high-level end-to-end quality specifications are satisfied during adaptive approximation tuning

A robust adaptive algebraic multigrid linear solver for structural mechanics

The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.Comment: 50 pages, 16 figures, submitted to CMAM

Control Studies of DFIG based Wind Power Systems

Wind energy as an outstanding and competitive form of renewable energy, has been growing fast worldwide in recent years because of its importance to reduce the pollutant emission generated by conventional thermal power plants and the rising prices and the unstable supplies of fossil-fuel. However, in the development of wind energy, there are still many ongoing challenges. An important challenge is the need of voltage control to maintain the terminal voltage of a wind plant to make it a PV bus like conventional generators with excitation control. In the literature with PI controllers used, the parameters of PI controllers need to be tuned as a tradeoff or compromise among various operating conditions. In this work, a new voltage control approach is presented. In the proposed approach, the PI control gains are dynamically adjusted based on the dynamic, continuous sensitivity which essentially indicates the dynamic relationship between the change of control gains and the desired output voltage. Hence, this control approach does not require any good estimation or tuning of fixed control gains because it has the self-learning mechanism via the dynamic sensitivity. This also gives the plug-and-play feature of DFIG controllers to make it promising in utility practices. Another key challenge in power regulation of wind energy is the control design in wind energy conversion system (WECS) to realize the tradeoff between the energy cost and control performance subject to stochastic wind speeds. In this work, the chance constraints are considered to address the control inputs and system outputs, as opposed to deterministic constraints in the literature, where the chance constraints include the stochastic behavior of the wind speed fluctuation. Two different control problems are considered here: The first one assumes the wind speed disturbance’s distribution is Gaussian; the second one assumes the disturbance is norm bounded, and the problem is formulated as a min-max optimization problem which has not been considered in the literature. Both problems are formulated as semi-definite program (SDP) optimization problems that can be solved efficiently with existing software tools. And simulation results are provided to demonstrate the validity of the proposed method