Design and optimization of a portable LQCD Monte Carlo code using OpenACC

The present panorama of HPC architectures is extremely heterogeneous, ranging from traditional multi-core CPU processors, supporting a wide class of applications but delivering moderate computing performance, to many-core GPUs, exploiting aggressive data-parallelism and delivering higher performances for streaming computing applications. In this scenario, code portability (and performance portability) become necessary for easy maintainability of applications; this is very relevant in scientific computing where code changes are very frequent, making it tedious and prone to error to keep different code versions aligned. In this work we present the design and optimization of a state-of-the-art production-level LQCD Monte Carlo application, using the directive-based OpenACC programming model. OpenACC abstracts parallel programming to a descriptive level, relieving programmers from specifying how codes should be mapped onto the target architecture. We describe the implementation of a code fully written in OpenACC, and show that we are able to target several different architectures, including state-of-the-art traditional CPUs and GPUs, with the same code. We also measure performance, evaluating the computing efficiency of our OpenACC code on several architectures, comparing with GPU-specific implementations and showing that a good level of performance-portability can be reached.Comment: 26 pages, 2 png figures, preprint of an article submitted for consideration in International Journal of Modern Physics

A design methodology for portable software on parallel computers

This final report for research that was supported by grant number NAG-1-995 documents our progress in addressing two difficulties in parallel programming. The first difficulty is developing software that will execute quickly on a parallel computer. The second difficulty is transporting software between dissimilar parallel computers. In general, we expect that more hardware-specific information will be included in software designs for parallel computers than in designs for sequential computers. This inclusion is an instance of portability being sacrificed for high performance. New parallel computers are being introduced frequently. Trying to keep one's software on the current high performance hardware, a software developer almost continually faces yet another expensive software transportation. The problem of the proposed research is to create a design methodology that helps designers to more precisely control both portability and hardware-specific programming details. The proposed research emphasizes programming for scientific applications. We completed our study of the parallelizability of a subsystem of the NASA Earth Radiation Budget Experiment (ERBE) data processing system. This work is summarized in section two. A more detailed description is provided in Appendix A ('Programming Practices to Support Eventual Parallelism'). Mr. Chrisman, a graduate student, wrote and successfully defended a Ph.D. dissertation proposal which describes our research associated with the issues of software portability and high performance. The list of research tasks are specified in the proposal. The proposal 'A Design Methodology for Portable Software on Parallel Computers' is summarized in section three and is provided in its entirety in Appendix B. We are currently studying a proposed subsystem of the NASA Clouds and the Earth's Radiant Energy System (CERES) data processing system. This software is the proof-of-concept for the Ph.D. dissertation. We have implemented and measured the performance of a portion of this subsystem on the Intel iPSC/2 parallel computer. These results are provided in section four. Our future work is summarized in section five, our acknowledgements are stated in section six, and references for published papers associated with NAG-1-995 are provided in section seven

Parallel Attribute Grammar Evaluation

Experiments with parallel compilation of programming languages are reported. In order to take advantage of the potential parallelism, the language translation process is expressed as an attribute grammar evaluation problem. Three primary benefits to using attribute grammars are noted. The efficiency and the potential for parallelism of various attribute grammar evaluation methods are studied, and the design of a combined evaluator, which seeks to combine the potential for concurrency of dynamic evaluators and the (sequential) efficiency of static evaluators, is outlined. The methods were used to generate a parallel compiler for a large Pascal subset

The CIAO Multi-Dialect Compiler and System: An Experimentation Workbench for Future (C)LP Systems

CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the áreas of program analysis and transformation already obtained with the system

Efficient Implementation of Estelle Specifications

Efficient implementation of communication software is of critical importance for high-speed networks. We analyze performance bottlenecks in existing implementations and propose two techniques for improvements: The first exploits parallelism not only in the actions of the FSMs, but also in the runtime system of the protocol stack. The second integrates adjacent layers leading to considerable savings in inter-layer interface handling and in the number of transitions occurring in the FSMs. Both techniques are discussed in the context of OSI upper layers, and are based on protocol specification in Estelle

A High-Performance Domain-Specific Language and Code Generator for General N-body Problems

General N-body problems are a set of problems in which an update to a single element in the system depends on every other element. N-body problems are ubiquitous, with applications in various domains ranging from scientific computing simulations in molecular dynamics, astrophysics, acoustics, and fluid dynamics all the way to computer vision, data mining and machine learning problems. Different N-body algorithms have been designed and implemented in these various fields. However, there is a big gap between the algorithm one designs on paper and the code that runs efficiently on a parallel system. It is time-consuming to write fast, parallel, and scalable code for these problems. On the other hand, the sheer scale and growth of modern scientific datasets necessitate exploiting the power of both parallel and approximation algorithms where there is a potential to trade-off accuracy for performance. The main problem that we are tackling in this thesis is how to automatically generate asymptotically optimal N-body algorithms from the high-level specification of the problem. We combine the body of work in performance optimizations, compilers and the domain of N-body problems to build a unified system where domain scientists can write programs at the high level while attaining performance of code written by an expert at the low level.In order to generate a high-performance, scalable code for this group of problems, we take the following steps in this thesis; first, we propose a unified algorithmic framework named PASCAL in order to address the challenge of designing a general algorithmic template to represent the class of N-body problems. PASCAL utilizes space-partitioning trees and user-controlled pruning/approximations to reduce the asymptotic runtime complexity from linear to logarithmic in the number of data points. In PASCAL, we design an algorithm that automatically generates conditions for pruning or approximation of an N-body problem considering the problem's definition. In order to evaluate PASCAL, we developed tree-based algorithms for six well-known problems: k-nearest neighbors, range search, minimum spanning tree, kernel density estimation, expectation maximization, and Hausdorff distance. We show that applying domain-specific optimizations and parallelization to the algorithms written in PASCAL achieves 10x to 230x speedup compared to state-of-the-art libraries on a dual-socket Intel Xeon processor with 16 cores on real-world datasets. Second, we extend the PASCAL framework to build PASCAL-X that adds support for NUMA-aware parallelization. PASCAL-X also presents insights on the influence of tuning parameters. Tuning parameters such as leaf size (influences the shape of the tree) and cut-off level (controls the granularity of tasks) of the space-partitioning trees result in performance improvement of up to 4.6x. A key goal is to generate scalable and high-performance code automatically without sacrificing productivity. That implies minimizing the effort the users have to put in to generate the desired high-performance code. Another critical factor is the adaptivity, which indicates the amount of effort that is required to extend the high-performance code generation to new N-body problems. Finally, we consider these factors and develop a domain-specific language and code generator named Portal, which is built on top of PASCAL-X. Portal's language design is inspired by the mathematical representation of N-body problems, resulting in an intuitive language for rapid implementation of a variety of problems. Portal's back-end is designed and implemented to generate optimized, parallel, and scalable implementations for multi-core systems. We demonstrate that the performance achieved by using Portal is comparable to that of expert hand-optimized code while providing productivity for domain scientists. For instance, using Portal for the k-nearest neighbors problem gains performance that is similar to the hand-optimized code, while reducing the lines of code by 68x. To the best of our knowledge, there are no known libraries or frameworks that implement parallel asymptotically optimal algorithms for the class of general N-body problems and this thesis primarily aims to fill this gap. Finally, we present a case study of Portal for the real-world problem of face clustering. In this case study, we show that Portal not only provides a fast solution for the face clustering problem with similar accuracy as the state-of-the-art algorithm, but also it provides productivity by implementing the face clustering algorithm in only 14 lines of Portal code

A new parallelisation technique for heterogeneous CPUs

Parallelization has moved in recent years into the mainstream compilers, and the demand for parallelizing tools that can do a better job of automatic parallelization is higher than ever. During the last decade considerable attention has been focused on developing programming tools that support both explicit and implicit parallelism to keep up with the power of the new multiple core technology. Yet the success to develop automatic parallelising compilers has been limited mainly due to the complexity of the analytic process required to exploit available parallelism and manage other parallelisation measures such as data partitioning, alignment and synchronization. This dissertation investigates developing a programming tool that automatically parallelises large data structures on a heterogeneous architecture and whether a high-level programming language compiler can use this tool to exploit implicit parallelism and make use of the performance potential of the modern multicore technology. The work involved the development of a fully automatic parallelisation tool, called VSM, that completely hides the underlying details of general purpose heterogeneous architectures. The VSM implementation provides direct and simple access for users to parallelise array operations on the Cell’s accelerators without the need for any annotations or process directives. This work also involved the extension of the Glasgow Vector Pascal compiler to work with the VSM implementation as a one compiler system. The developed compiler system, which is called VP-Cell, takes a single source code and parallelises array expressions automatically. Several experiments were conducted using Vector Pascal benchmarks to show the validity of the VSM approach. The VP-Cell system achieved significant runtime performance on one accelerator as compared to the master processor’s performance and near-linear speedups over code runs on the Cell’s accelerators. Though VSM was mainly designed for developing parallelising compilers it also showed a considerable performance by running C code over the Cell’s accelerators