Porting the Sisal functional language to distributed-memory multiprocessors

Parallel computing is becoming increasingly ubiquitous in recent years. The sizes of application problems continuously increase for solving real-world problems. Distributed-memory multiprocessors have been regarded as a viable architecture of scalable and economical design for building large scale parallel machines. While these parallel machines can provide computational capabilities, programming such large-scale machines is often very difficult due to many practical issues including parallelization, data distribution, workload distribution, and remote memory latency. This thesis proposes to solve the programmability and performance issues of distributed-memory machines using the Sisal functional language. The programs written in Sisal will be automatically parallelized, scheduled and run on distributed-memory multiprocessors with no programmer intervention. Specifically, the proposed approach consists of the following steps. Given a program written in Sisal, the front end Sisal compiler generates a directed acyclic graph(DAG) to expose parallelism in the program. The DAG is partitioned and scheduled based on loop parallelism. The scheduled DAG is then translated to C programs with machine specific parallel constructs. The parallel C programs are finally compiled by the target machine specific compilers to generate executables. A distributed-memory parallel machine, the 80-processor ETL EM-X, has been chosen to perform experiments. The entire procedure has been implemented on the EMX multiprocessor. Four problems are selected for experiments: bitonic sorting, search, dot-product and Fast Fourier Transform. Preliminary execution results indicate that automatic parallelization of the Sisal programs based on loop parallelism is effective. The speedup for these four problems is ranging from 17 to 60 on a 64-processor EM-X. Preliminary experimental results further indicate that programming distributed-memory multiprocessors using a functional language indeed frees the programmers from lowl-evel programming details while allowing them to focus on algorithmic performance improvement

Theoretical study of the effect of fibre orientations and porosity on heat conductivity of reinforced polymer composites

In recent years, there has been an increasing demand for engineering materials which not only possess good mechanical and thermal properties but are also cheap and environmentally friendly. Composites are unique engineering materials which can be tailor made from a large variety of materials to suit specific applications. Composites primarily consist of a polymer resin matrix in which other material is incorporated in discrete units for reinforcing. The reinforcing materials can be in the form of fibres or flakes orientated in various ways to impart maximum performance. Natural fibres such as sisal, kenaf, bagasse, hemp etc. have been studied as reinforcing material for conventional polymer resins. Such composites are often termed green composites and they have unique mechanical properties when compared to conventional composites. They are also available at a cheap price and weigh a lot less. In addition, they can also offer unique thermal and acoustic insulation properties. Due to these attractive features they are used in the automotive, aerospace, textile and construction industries. A particularly important feature which determines the properties of natural fibre composites and their porosity. From an industrial and academic point of view, there is a need to study the heat conductivity of newly developed composites. This is influenced by the porosity of the composite. This project, investigated the effect of porosity and their orientation on the heat conductivity of polymer composites. Experimental and theoretical studies were conducted on mainly sisal-glass fibre polymer composites. Different volume of fibre fractions were tested in this study. It was expected that the presence of the fibres would dramatically improve the heat conductivity properties of the materials because the sisal fibres have internal porosity. The results of this work are expect to contribute to academic and industrial knowledge about the thermal performance of fibre composites. The data will be published in a professional journal. This knowledge will contribute to the manufacturing of newly developed materials for industrial applications

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

An implementation of SISAL for distributed-memory architectures

This thesis describes a new implementation of the implicitly parallel functional programming language SISAL, for massively parallel processor supercomputers. The Optimizing SISAL Compiler (OSC), developed at Lawrence Livermore National Laboratory, was originally designed for shared-memory multiprocessor machines and has been adapted to distributed-memory architectures. OSC has been relatively portable between shared-memory architectures, because they are architecturally similar, and OSC generates portable C code. However, distributed-memory architectures are not standardized -- each has a different programming model. Distributed-memory SISAL depends on a layer of software that provides a portable, distributed, shared-memory abstraction. This layer is provided by Split-C, a dialect of the C programming language developed at U.C. Berkeley, which has demonstrated good performance on distributed-memory architectures. Split-C provides important capabilities for good performance: support for program-specific distributed data structures, and split-phase memory operations. Distributed data structures help achieve good memory locality, while split-phase memory operations help tolerate the longer communication latencies inherent in distributed-memory architectures. The distributed-memory SISAL compiler and run-time system takes advantage of these capabilities. The results of these efforts is a compiler that runs identically on the Thinking Machines Connection Machine (CM-5), and the Meiko Computing Surface (CS-2)

An interface between single assignment C and vector Pascal

This dissertation contains an overview of the research I’ve been doing over in Glasgow University, which is mainly a project of developing an interface between two array programming languages, Single Assignment C and Vector Pascal, to combine them together by using the Vector Pascal code generator for Single Assignment C. Single Assignment C provides support for multi-threading but it doesn’t contain any utilization of SIMD technology, and Vector Pascal implements array operations with the help of SIMD instruction sets of modern general processors. Thus my hypothesis is that this combination will let the program enjoy higher run-time performance compared to the one which is only compiled by using Single Assignment C’s compiler. This dissertation explains the detail of designing and implementing this interface between these two languages; and the system to manipulate the three parts, i.e. the interface and the two languages’ compilers together to make them work automatically. The interface is generally developed based on traversal over Syntax Tree and involves works of vectorization and loop unrolling. Meanwhile, a benchmark testing system to validate my hypothesis is created and introduced in this dissertation too, which is accompanied with the testing results and analysis

Distributed, parallel web service orchestration using XSLT

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.GridXSLT is an implementation of the XSLT programming language designed for distributed web service orchestration. Based on the functional semantics of the language, it compiles programs into dataflow graphs which can be efficiently executed across a collection of machines in a cluster or grid environment. Calls to web services can be made using the standard function call semantics provided by the language, and occur in parallel using the dataflow model of computation. The programmer is not required to explicitly specify the parallelism, as the details of how programs are scheduled and executed in a distributed environment are abstracted away by the run-time engine. XSLT provides a higher level programming model than many other approaches to web services composition; we explore its use here as a means of easing the task of orchestrating the interactions between services. In addition to the normal XSLT syntax, our system also supports programs written in XSLiTe, an alternative syntax we have developed which uses more concise representations of language constructs, increasing the ease of development, and bringing code readability closer to that of traditional programming languages. Our goal is to ease the construction of applications based on web services composition, such as those used in eScience and other fields in which service oriented architectures are prominent.Peter M. Kelly, Paul D. Coddington, Andrew L. Wendelbor