Redesigning OP2 Compiler to Use HPX Runtime Asynchronous Techniques

Maximizing parallelism level in applications can be achieved by minimizing overheads due to load imbalances and waiting time due to memory latencies. Compiler optimization is one of the most effective solutions to tackle this problem. The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided with a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and producing desired application scalability. One solution to address this challenge is the use of runtime methods. This strategy can be implemented by delaying certain amount of code analysis to be done at runtime. In this research, we improve the parallel application performance generated by the OP2 compiler by leveraging HPX, a C++ runtime system, to provide runtime optimizations. These optimizations include asynchronous tasking, loop interleaving, dynamic chunk sizing, and data prefetching. The results of the research were evaluated using an Airfoil application which showed a 40-50% improvement in parallel performance.Comment: 18th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2017

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

Automatic Parallelization With Statistical Accuracy Bounds

Traditional parallelizing compilers are designed to generate parallel programs that produce identical outputs as the original sequential program. The difficulty of performing the program analysis required to satisfy this goal and the restricted space of possible target parallel programs have both posed significant obstacles to the development of effective parallelizing compilers. The QuickStep compiler is instead designed to generate parallel programs that satisfy statistical accuracy guarantees. The freedom to generate parallel programs whose output may differ (within statistical accuracy bounds) from the output of the sequential program enables a dramatic simplification of the compiler and a significant expansion in the range of parallel programs that it can legally generate. QuickStep exploits this flexibility to take a fundamentally different approach from traditional parallelizing compilers. It applies a collection of transformations (loop parallelization, loop scheduling, synchronization introduction, and replication introduction) to generate a search space of parallel versions of the original sequential program. It then searches this space (prioritizing the parallelization of the most time-consuming loops in the application) to find a final parallelization that exhibits good parallel performance and satisfies the statistical accuracy guarantee. At each step in the search it performs a sequence of trial runs on representative inputs to examine the performance, accuracy, and memory accessing characteristics of the current generated parallel program. An analysis of these characteristics guides the steps the compiler takes as it explores the search space of parallel programs. Results from our benchmark set of applications show that QuickStep can automatically generate parallel programs with good performance and statistically accurate outputs. For two of the applications, the parallelization introduces noise into the output, but the noise remains within acceptable statistical bounds. The simplicity of the compilation strategy and the performance and statistical acceptability of the generated parallel programs demonstrate the advantages of the QuickStep approach

Parallelizing Sequential Programs With Statistical Accuracy Tests

We present QuickStep, a novel system for parallelizing sequential programs. QuickStep deploys a set of parallelization transformations that together induce a search space of candidate parallel programs. Given a sequential program, representative inputs, and an accuracy requirement, QuickStep uses performance measurements, profiling information, and statistical accuracy tests on the outputs of candidate parallel programs to guide its search for a parallelizationthat maximizes performance while preserving acceptable accuracy. When the search completes, QuickStep produces an interactive report that summarizes the applied parallelization transformations, performance, and accuracy results for the automatically generated candidate parallel programs. In our envisioned usage scenarios, the developer examines this report to evaluate the acceptability of the final parallelization and to obtain insight into how the original sequential program responds to different parallelization strategies. Itis also possible for the developer (or even a user of the program who has no software development expertise whatsoever) to simply use the best parallelization out of the box without examining the report or further investigating the parallelization. Results from our benchmark set of applications show that QuickStep can automatically generate accurate and efficient parallel programs---the automatically generated parallel versions of five of our six benchmark applications run between 5.0 and 7.7 times faster on 8 cores than the original sequential versions. Moreover, a comparison with the Intel icc compiler highlights how QuickStep can effectively parallelize applications with features (such as the use of modern object-oriented programming constructs or desirable parallelizations with infrequent but acceptable data races) that place them inherently beyond the reach of standard approaches

Run-time optimization of adaptive irregular applications

Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems

A hybrid MPI-OpenMP scheme for scalable parallel pseudospectral computations for fluid turbulence

A hybrid scheme that utilizes MPI for distributed memory parallelism and OpenMP for shared memory parallelism is presented. The work is motivated by the desire to achieve exceptionally high Reynolds numbers in pseudospectral computations of fluid turbulence on emerging petascale, high core-count, massively parallel processing systems. The hybrid implementation derives from and augments a well-tested scalable MPI-parallelized pseudospectral code. The hybrid paradigm leads to a new picture for the domain decomposition of the pseudospectral grids, which is helpful in understanding, among other things, the 3D transpose of the global data that is necessary for the parallel fast Fourier transforms that are the central component of the numerical discretizations. Details of the hybrid implementation are provided, and performance tests illustrate the utility of the method. It is shown that the hybrid scheme achieves near ideal scalability up to ~20000 compute cores with a maximum mean efficiency of 83%. Data are presented that demonstrate how to choose the optimal number of MPI processes and OpenMP threads in order to optimize code performance on two different platforms.Comment: Submitted to Parallel Computin