1,927 research outputs found
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems
As we approach the exascale era, the size and complexity of HPC systems
continues to increase, raising concerns about their manageability and
sustainability. For this reason, more and more HPC centers are experimenting
with fine-grained monitoring coupled with Operational Data Analytics (ODA) to
optimize efficiency and effectiveness of system operations. However, while
monitoring is a common reality in HPC, there is no well-stated and
comprehensive list of requirements, nor matching frameworks, to support
holistic and online ODA. This leads to insular ad-hoc solutions, each
addressing only specific aspects of the problem.
In this paper we propose Wintermute, a novel generic framework to enable
online ODA on large-scale HPC installations. Its design is based on the results
of a literature survey of common operational requirements. We implement
Wintermute on top of the holistic DCDB monitoring system, offering a large
variety of configuration options to accommodate the varying requirements of ODA
applications. Moreover, Wintermute is based on a set of logical abstractions to
ease the configuration of models at a large scale and maximize code re-use. We
highlight Wintermute's flexibility through a series of practical case studies,
each targeting a different aspect of the management of HPC systems, and then
demonstrate the small resource footprint of our implementation.Comment: Accepted for publication at the 29th ACM International Symposium on
High-Performance Parallel and Distributed Computing (HPDC 2020
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Optimizing Data-Intensive Computing with Efficient Configuration Tuning
As the complexity of distributed analytics systems evolves over time, more configuration parameters get exposed for tuning. While these numerous parameters allow users more control over how their workloads are executed, this flexibility comes at a cost, since finding the right configurations for such systems in a cost-effective way becomes challenging. In practice, several factors contribute to the complexity of tuning the configuration of those systems: the large configuration space, the diversity of the served workloads (each workload possibly requiring a different resource allocation strategy to run optimally), and the dynamic
characteristics of these systems’ environment (e.g., increase in input data size, changes in the allocation of resources). Paradoxically, existing solutions for workload tuning either assume static tuning environment or workloads that are inexpensive to run (i.e. requiring hundreds of execution samples). Recently, Bayesian Optimisation (BO) strategies have been applied as a solution to enable efficient autotuning. They build a probabilistic model incrementally to predict the impact of the parameters on performance using a small number of execution samples. The incrementally constructed BO model is used to guide the tuning process and accelerate convergence to a near-optimal configuration. Unfortunately, for distributed analytics systems, the configuration space is too large to construct a good model using traditional BO, which fails to provide quick convergence in high dimensional configuration space.
I argue that cost-effective tuning strategies can only be developed when taking into account: the frequent changes that can happen in the analytics workload/environment, the amortization of tuning costs and how this influences tuning profitability, the high dimensionality of configuration
space and the need to cater for diverse workloads. To tackle these challenges, I propose Tuneful, an efficient configuration tuning framework
for such expensive to tune systems. It works efficiently both initially (when little data is available) as well as later (as more tuning knowledge is acquired). It starts with learning workload-specific influential parameters incrementally and tunes those only, then when more tuning knowledge becomes available, it detects similarity across workloads and utilizes multitask BO to share the tuning knowledge across similar workloads. I show how augmenting the BO approach with parameters’ significance and workload similarity characteristics enables an
efficient configuration tuning in high dimensional configuration space. Over diverse analytics workloads, this significantly accelerates both configuration tuning and cost amortization, saving search time by 2.7-3.7X at median compared to the-state-of-the-art approaches
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