A Domain Specific Approach to High Performance Heterogeneous Computing

Users of heterogeneous computing systems face two problems: firstly, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and secondly, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program, which can be solved using heuristics, machine learning or Mixed Integer Linear Programming (MILP) frameworks. These claims are illustrated using the example domain of derivatives pricing in computational finance, with the domain metrics of workload latency or makespan and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10% of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio

Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service

In the near future FPGAs will be available by the hour, however this new Infrastructure as a Service (IaaS) usage mode presents both an opportunity and a challenge: The opportunity is that programmers can potentially trade resources for performance on a much larger scale, for much shorter periods of time than before. The challenge is in finding and traversing the trade-off for heterogeneous IaaS that guarantees increased resources result in the greatest possible increased performance. Such a trade-off is Pareto optimal. The Pareto optimal trade-off for clusters of heterogeneous resources can be found by solving multiple, multi-objective optimisation problems, resulting in an optimal allocation of tasks to the available platforms. Solving these optimisation programs can be done using simple heuristic approaches or formal Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up to 110% faster than a heuristic approach, and over 50% cheaper. These results suggest that high quality performance-resource trade-offs of heterogeneous IaaS are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software Programmers (FSP 2015) (arXiv:1508.06320

Putting the pieces together: the systematic development of a software defined radio toolflow for the Rhino project

This dissertation is concerned with the thesis that it is possible for a software defined radio system that has been described in accordance with synchronous data flow theory to be implemented upon a reconfigurable computing platform

Ubiquitous Sensor Networking for Development (USN4D):An Application to Pollution Monitoring

This paper presents a new Ubiquitous Sensor Network (USN) Architecture to be used in developing countries and reveals its usefulness by highlighting some of its key features. In complement to a previous ITU proposal, our architecture referred to as “Ubiquitous Sensor Network for Development (USN4D)” integrates in its layers features such as opportunistic data dissemination, long distance deployment and localisation of information to meet the requirements of the developing world. Besides describing some of the most important requirements for the sensor equipment to be used in a USN4D setting, we present the main features and experiments conducted using the “WaspNet” as one of the wireless sensor deployment platforms that meets these requirements. Furthermore, building upon “WaspNet” platform, we present an application to Air pollution Monitoring in the city of Cape Town, in South Africa as one of the first steps towards building community wireless sensor networks (CSN) in the developing world using off-the-shelf sensor equipment

On the Relevance of Using Open Wireless Sensor Networks in Environment Monitoring

This paper revisits the problem of the readiness for field deployments of wireless sensor networks by assessing the relevance of using Open Hardware and Software motes for environment monitoring. We propose a new prototype wireless sensor network that fine-tunes SquidBee motes to improve the life-time and sensing performance of an environment monitoring system that measures temperature, humidity and luminosity. Building upon two outdoor sensing scenarios, we evaluate the performance of the newly proposed energy-aware prototype solution in terms of link quality when expressed by the Received Signal Strength, Packet Loss and the battery lifetime. The experimental results reveal the relevance of using the Open Hardware and Software motes when setting up outdoor wireless sensor networks

Portable, predictable and partitionable: a domain specific approach to heterogeneous computing

Computing is increasingly heterogeneous. Beyond Central Processing Units (CPUs), different architectures such as massively parallel Graphics Processing Units (GPUs) and reconfigurable Field Programmable Gate Arrays (FPGAs) are seeing widespread adoption. However, the failure of conventional programming approaches to support portable execution, predict the runtime characteristics and partition workloads optimally is hindering the realisation of heterogeneous computing. By narrowing the scope of expression in a natural manner, using a domain specific approach, these three challenges can be addressed. A domain specific heterogeneous computing methodology enables three features: Portability, Prediction and Partitioning. Portable, efficient execution is enabled by a domain specific approach because only a subset of domain functions need to be supported across the heterogeneous computing platforms. Predictive models of runtime characteristics are enabled as the structure of the domain functions may be analysed a priori. Finally optimal partitioning is possible because the metric models can be used to form an optimisation program that can be solved by either heuristic, machine learning or Mixed Integer Linear Programming (MILP) approaches. Using the example of the application domain of financial derivatives pricing, a domain specific application framework, the Forward Financial Framework (F^3), can execute a single pricing task upon a diverse range of CPU, GPU and FPGA platforms from many different vendors. Not only do these portable implementations exhibit strong parallel scaling, but are competitive with state-of-the-art, expert created implementations of the same option pricing problems. Furthermore, F^3 can model the crucial runtime metrics of latency and accuracy for these heterogeneous platforms using a small benchmarking procedure to within 10% of the run-time value of these metrics. Finally, the framework can optimally partition work across heterogeneous platforms, using a MILP framework, that is up to 270 times more efficient than what is achieved by using a heuristic approach.Open Acces