Efficient update of ghost regions using active messages

The use of ghost regions is a common feature of many distributed grid applications. A ghost region holds local read-only copies of remotely-held boundary data which are exchanged and cached many times over the course of a computation. X10 is a modern par

X10 for high-performance scientific computing

High performance computing is a key technology that enables large-scale physical simulation in modern science. While great advances have been made in methods and algorithms for scientific computing, the most commonly used programming models encourage a fragmented view of computation that maps poorly to the underlying computer architecture. Scientific applications typically manifest physical locality, which means that interactions between entities or events that are nearby in space or time are stronger than more distant interactions. Linear-scaling methods exploit physical locality by approximating distant interactions, to reduce computational complexity so that cost is proportional to system size. In these methods, the computation required for each portion of the system is different depending on that portion’s contribution to the overall result. To support productive development, application programmers need programming models that cleanly map aspects of the physical system being simulated to the underlying computer architecture while also supporting the irregular workloads that arise from the fragmentation of a physical system. X10 is a new programming language for high-performance computing that uses the asynchronous partitioned global address space (APGAS) model, which combines explicit representation of locality with asynchronous task parallelism. This thesis argues that the X10 language is well suited to expressing the algorithmic properties of locality and irregular parallelism that are common to many methods for physical simulation. The work reported in this thesis was part of a co-design effort involving researchers at IBM and ANU in which two significant computational chemistry codes were developed in X10, with an aim to improve the expressiveness and performance of the language. The first is a Hartree–Fock electronic structure code, implemented using the novel Resolution of the Coulomb Operator approach. The second evaluates electrostatic interactions between point charges, using either the smooth particle mesh Ewald method or the fast multipole method, with the latter used to simulate ion interactions in a Fourier Transform Ion Cyclotron Resonance mass spectrometer. We compare the performance of both X10 applications to state-of-the-art software packages written in other languages. This thesis presents improvements to the X10 language and runtime libraries for managing and visualizing the data locality of parallel tasks, communication using active messages, and efficient implementation of distributed arrays. We evaluate these improvements in the context of computational chemistry application examples. This work demonstrates that X10 can achieve performance comparable to established programming languages when running on a single core. More importantly, X10 programs can achieve high parallel efficiency on a multithreaded architecture, given a divide-and-conquer pattern parallel tasks and appropriate use of worker-local data. For distributed memory architectures, X10 supports the use of active messages to construct local, asynchronous communication patterns which outperform global, synchronous patterns. Although point-to-point active messages may be implemented efficiently, productive application development also requires collective communications; more work is required to integrate both forms of communication in the X10 language. The exploitation of locality is the key insight in both linear-scaling methods and the APGAS programming model; their combination represents an attractive opportunity for future co-design efforts

An Optimization Theory for Structured Stencil-based Parallel Applications

In this thesis, we introduce a new optimization theory for stencil-based applications which is centered both on a modification of the well known owner-computes rule and on base but powerful properties oftoroidal spaces. The proposed optimization techniques provide notable results in different computational aspects: from the reduction of communication overhead to the reduction of computation time, through the minimization of memory requirement without performance loss. All classical optimization theory is based on defining transformations that can produce optimized programs which are computationally equivalent to the original ones. According to Kennedy, two programs are equivalent if, from the same input data, they produce identical output data. As other proposed modifications to the owner-computes rule, we exploit stencil application feature of being characterized by a set of consecutive steps. For such configurations, it is possible to define specific two phase optimizations. The first phase is characterized by the application of program transformations which result in an efficient computation of an output that be easily converted into the original one. In other words the transformed program defined by the first phase is not computational equivalent with respect to the original one. The second phase converts the output of the previous phase back into the original one exploiting optimized technique in order to introduce the lowest additional overhead. The phase guarantees the computational equivalence of the approach. Obviously, in order to define an interesting new optimization technique, we have to prove that the overall performance of the two phases sequence is greater than the one of the original program. Exploiting a structured approach and studying this optimization theory on stencils featuring specific patterns of functional dependencies, we discover a set of novel transformations which result in significant optimizations. Among the new transformations, the most notable one, which aims to reduce the number of communications necessary to implement a stencil-based application, turns out to be the best optimization technique amongst those cited in the literature. All the improvements provided by transformations presented in this thesis have been both formally proved and experimentally tested on an heterogeneous set of architectures including clusters and different types of multi-cores

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

The readying of applications for heterogeneous computing

High performance computing is approaching a potentially significant change in architectural design. With pressures on the cost and sheer amount of power, additional architectural features are emerging which require a re-think to the programming models deployed over the last two decades. Today's emerging high performance computing (HPC) systems are maximising performance per unit of power consumed resulting in the constituent parts of the system to be made up of a range of different specialised building blocks, each with their own purpose. This heterogeneity is not just limited to the hardware components but also in the mechanisms that exploit the hardware components. These multiple levels of parallelism, instruction sets and memory hierarchies, result in truly heterogeneous computing in all aspects of the global system. These emerging architectural solutions will require the software to exploit tremendous amounts of on-node parallelism and indeed programming models to address this are emerging. In theory, the application developer can design new software using these models to exploit emerging low power architectures. However, in practice, real industrial scale applications last the lifetimes of many architectural generations and therefore require a migration path to these next generation supercomputing platforms. Identifying that migration path is non-trivial: With applications spanning many decades, consisting of many millions of lines of code and multiple scientific algorithms, any changes to the programming model will be extensive and invasive and may turn out to be the incorrect model for the application in question. This makes exploration of these emerging architectures and programming models using the applications themselves problematic. Additionally, the source code of many industrial applications is not available either due to commercial or security sensitivity constraints. This thesis highlights this problem by assessing current and emerging hard- ware with an industrial strength code, and demonstrating those issues described. In turn it looks at the methodology of using proxy applications in place of real industry applications, to assess their suitability on the next generation of low power HPC offerings. It shows there are significant benefits to be realised in using proxy applications, in that fundamental issues inhibiting exploration of a particular architecture are easier to identify and hence address. Evaluations of the maturity and performance portability are explored for a number of alternative programming methodologies, on a number of architectures and highlighting the broader adoption of these proxy applications, both within the authors own organisation, and across the industry as a whole