1,479 research outputs found
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
Using Statistical Assertions to Guide Self-Adaptive Systems
Self-adaptive systems need to monitor themselves, to check their internal behaviour and design assumptions about runtime inputs and conditions. This kind of monitoring for self-adaptive systems can include collecting statistics about such systems themselves which can be computationally intensive (for detailed statistics) and hence time consuming, with possible negative impact on self-adaptive response time. To mitigate this limitation, we extend the technique of in-circuit runtime assertions to cover statistical assertions in hardware. The presented designs implement several statistical operators that can be exploited by self-adaptive systems; a novel optimization is developed for reducing the number of pairwise operators from ON to OlogN. To illustrate the practicability and industrial relevance of our proposed approach, we evaluate our designs, chosen from a class of possible application scenarios, for their resource usage and the tradeoffs between hardware and software implementations
A Comment on the Implementation of the Ziggurat Method
We show that the short period of the uniform random number generator in the published implementation of Marsaglia and Tsang's Ziggurat method for generating random deviates can lead to poor distributions. Changing the uniform random number generator used in its implementation fixes this issue.
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