PList-based Divide and Conquer Parallel Programming

This paper details an extension of a Java parallel programming framework – JPLF. The JPLF framework is a programming framework that helps programmers build parallel programs using existing building blocks. The framework is based on {em PowerLists} and PList Theories and it naturally supports multi-way Divide and Conquer. By using this framework, the programmer is exempted from dealing with all the complexities of writing parallel programs from scratch. This extension to the JPLF framework adds PLists support to the framework and so, it enlarges the applicability of the framework to a larger set of parallel solvable problems. Using this extension, we may apply more flexible data division strategies. In addition, the length of the input lists no longer has to be a power of two – as required by the PowerLists theory. In this paper we unveil new applications that emphasize the new class of computations that can be executed within the JPLF framework. We also give a detailed description of the data structures and functions involved in the PLists extension of the JPLF, and extended performance experiments are described and analyzed

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

Multi-core platforms have spread to all corners of the computing industry, and trends in design and power indicate that the shift to multi-core will become even wider-spread in the future. As the number of cores on a chip rises, the complexity of memory systems and on-chip interconnects increases drastically. The programmer inherits this complexity in the form of new responsibilities for task decomposition, synchronization, and data movement within an application, which hitherto have been concealed by complex processing pipelines or deemed unimportant since tasks were largely executed sequentially. To some extent, the need for explicit parallel programming is inevitable, due to limits in the instruction-level parallelism that can be automatically extracted from a program. However, these challenges create a great opportunity for the development of new programming abstractions which hide the low-level architectural complexity while exposing intuitive high-level mechanisms for expressing parallelism. Many models of parallel programming fall into the category of data-centric models, where the structure of an application depends on the role of data and communication in the relationships between tasks. The utilization of the inter-core communication networks and effective scaling to large data sets are decidedly important in designing efficient implementations of parallel applications. The questions of how many low-level architectural details should be exposed to the programmer, and how much parallelism in an application a programmer should expose to the compiler remain open-ended, with different answers depending on the architecture and the application in question. I propose that the key to unlocking the capabilities of multi-core platforms is the development of abstractions and optimizations which match the patterns of data movement in applications with the inter-core communication capabilities of the platforms. After a comparative analysis that confirms and stresses the importance of finding a good match between the programming abstraction, the application, and the architecture, this dissertation proposes two techniques that showcase the power of leveraging data dependency patterns in parallel performance optimizations. Flexible Filters dynamically balance load in stream programs by creating flexibility in the runtime data flow through the addition of redundant stream filters. This technique combines a static mapping with dynamic flow control to achieve light-weight, distributed and scalable throughput optimization. The properties of stream communication, i.e., FIFO pipes, enable flexible filters by exposing the backpressure dependencies between tasks. Next, I present Huckleberry, a novel recursive programming abstraction developed in order to allow programmers to expose data locality in divide-and-conquer algorithms at a high level of abstraction. Huckleberry automatically converts sequential recursive functions with explicit data partitioning into parallel implementations that can be ported across changes in the underlying architecture including the number of cores and the amount of on-chip memory. I then present a performance model for multi-core applications which provides an efficient means to evaluate the trade-offs between the computational and communication requirements of applications together with the hardware resources of a target multi-core architecture. The model encompasses all data-driven abstractions that can be reduced to a task graph representation and is extensible to performance techniques such as Flexible Filters that alter an application's original task graph. Flexible Filters and Huckleberry address the challenges of parallel programming on multi-core architectures by taking advantage of properties specific to the stream and recursive paradigms, and the performance model creates a unifying framework based on the communication between tasks in parallel applications. Combined, these contributions demonstrate that specialization with respect to communication patterns enhances the ability of parallel programming abstractions and optimizations to harvest the power of multi-core platforms

Structured arrows : a type-based framework for structured parallelism

This thesis deals with the important problem of parallelising sequential code. Despite the importance of parallelism in modern computing, writing parallel software still relies on many low-level and often error-prone approaches. These low-level approaches can lead to serious execution problems such as deadlocks and race conditions. Due to the non-deterministic behaviour of most parallel programs, testing parallel software can be both tedious and time-consuming. A way of providing guarantees of correctness for parallel programs would therefore provide significant benefit. Moreover, even if we ignore the problem of correctness, achieving good speedups is not straightforward, since this generally involves rewriting a program to consider a (possibly large) number of alternative parallelisations. This thesis argues that new languages and frameworks are needed. These language and frameworks must not only support high-level parallel programming constructs, but must also provide predictable cost models for these parallel constructs. Moreover, they need to be built around solid, well-understood theories that ensure that: (a) changes to the source code will not change the functional behaviour of a program, and (b) the speedup obtained by doing the necessary changes is predictable. Algorithmic skeletons are parametric implementations of common patterns of parallelism that provide good abstractions for creating new high-level languages, and also support frameworks for parallel computing that satisfy the correctness and predictability requirements that we require. This thesis presents a new type-based framework, based on the connection between structured parallelism and structured patterns of recursion, that provides parallel structures as type abstractions that can be used to statically parallelise a program. Specifically, this thesis exploits hylomorphisms as a single, unifying construct to represent the functional behaviour of parallel programs, and to perform correct code rewritings between alternative parallel implementations, represented as algorithmic skeletons. This thesis also defines a mechanism for deriving cost models for parallel constructs from a queue-based operational semantics. In this way, we can provide strong static guarantees about the correctness of a parallel program, while simultaneously achieving predictable speedups.“This work was supported by the University of St Andrews (School of Computer Science); by the EU FP7 grant “ParaPhrase:Parallel Patterns Adaptive Heterogeneous Multicore Systems” (n. 288570); by the EU H2020 grant “RePhrase: Refactoring Parallel Heterogeneous Resource-Aware Applications - a Software Engineering Approach” (ICT-644235), by COST Action IC1202 (TACLe), supported by COST (European Cooperation Science and Technology); and by EPSRC grant “Discovery: Pattern Discovery and Program Shaping for Manycore Systems” (EP/P020631/1)” -- Acknowledgement