191 research outputs found
The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing
The risk of reinsurance portfolios covering globally occurring natural
catastrophes, such as earthquakes and hurricanes, is quantified by employing
simulations. These simulations are computationally intensive and require large
amounts of data to be processed. The use of many-core hardware accelerators,
such as the Intel Xeon Phi and the NVIDIA Graphics Processing Unit (GPU), are
desirable for achieving high-performance risk analytics. In this paper, we set
out to investigate how accelerators can be employed in risk analytics, focusing
on developing parallel algorithms for Aggregate Risk Analysis, a simulation
which computes the Probable Maximum Loss of a portfolio taking both primary and
secondary uncertainties into account. The key result is that both hardware
accelerators are useful in different contexts; without taking data transfer
times into account the Phi had lowest execution times when used independently
and the GPU along with a host in a hybrid platform yielded best performance.Comment: A modified version of this article is accepted to the Computers and
Electrical Engineering Journal under the title - "The Hardware Accelerator
Debate: A Financial Risk Case Study Using Many-Core Computing"; Blesson
Varghese, "The Hardware Accelerator Debate: A Financial Risk Case Study Using
Many-Core Computing," Computers and Electrical Engineering, 201
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Data Challenges in High-Performance Risk Analytics
Risk Analytics is important to quantify, manage and analyse risks from the
manufacturing to the financial setting. In this paper, the data challenges in
the three stages of the high-performance risk analytics pipeline, namely risk
modelling, portfolio risk management and dynamic financial analysis is
presented
Cloud Services Brokerage: A Survey and Research Roadmap
A Cloud Services Brokerage (CSB) acts as an intermediary between cloud
service providers (e.g., Amazon and Google) and cloud service end users,
providing a number of value adding services. CSBs as a research topic are in
there infancy. The goal of this paper is to provide a concise survey of
existing CSB technologies in a variety of areas and highlight a roadmap, which
details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud
Computing (CLOUD 2015
Executing Bag of Distributed Tasks on the Cloud: Investigating the Trade-offs Between Performance and Cost
Bag of Distributed Tasks (BoDT) can benefit from decentralised execution on
the Cloud. However, there is a trade-off between the performance that can be
achieved by employing a large number of Cloud VMs for the tasks and the
monetary constraints that are often placed by a user. The research reported in
this paper is motivated towards investigating this trade-off so that an optimal
plan for deploying BoDT applications on the cloud can be generated. A heuristic
algorithm, which considers the user's preference of performance and cost is
proposed and implemented. The feasibility of the algorithm is demonstrated by
generating execution plans for a sample application. The key result is that the
algorithm generates optimal execution plans for the application over 91\% of
the time
Executing Bag of Distributed Tasks on Virtually Unlimited Cloud Resources
Bag-of-Distributed-Tasks (BoDT) application is the collection of identical
and independent tasks each of which requires a piece of input data located
around the world. As a result, Cloud computing offers an ef- fective way to
execute BoT application as it not only consists of multiple geographically
distributed data centres but also allows a user to pay for what she actually
uses only. In this paper, BoDT on the Cloud using virtually unlimited cloud
resources. A heuristic algorithm is proposed to find an execution plan that
takes budget constraints into account. Compared with other approaches, with the
same given budget, our algorithm is able to reduce the overall execution time
up to 50%
Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches
Background: Large-scale biological jobs on high-performance computing systems
require manual intervention if one or more computing cores on which they
execute fail. This places not only a cost on the maintenance of the job, but
also a cost on the time taken for reinstating the job and the risk of losing
data and execution accomplished by the job before it failed. Approaches which
can proactively detect computing core failures and take action to relocate the
computing core's job onto reliable cores can make a significant step towards
automating fault tolerance.
Method: This paper describes an experimental investigation into the use of
multi-agent approaches for fault tolerance. Two approaches are studied, the
first at the job level and the second at the core level. The approaches are
investigated for single core failure scenarios that can occur in the execution
of parallel reduction algorithms on computer clusters. A third approach is
proposed that incorporates multi-agent technology both at the job and core
level. Experiments are pursued in the context of genome searching, a popular
computational biology application.
Result: The key conclusion is that the approaches proposed are feasible for
automating fault tolerance in high-performance computing systems with minimal
human intervention. In a typical experiment in which the fault tolerance is
studied, centralised and decentralised checkpointing approaches on an average
add 90% to the actual time for executing the job. On the other hand, in the
same experiment the multi-agent approaches add only 10% to the overall
execution time.Comment: Computers in Biology and Medicin
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