2,613 research outputs found
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
A methodology for full-system power modeling in heterogeneous data centers
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener-
alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft
Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs
In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain
A Fast and Accurate Cost Model for FPGA Design Space Exploration in HPC Applications
Heterogeneous High-Performance Computing
(HPC) platforms present a significant programming challenge,
especially because the key users of HPC resources are scientists,
not parallel programmers. We contend that compiler technology
has to evolve to automatically create the best program variant
by transforming a given original program. We have developed a
novel methodology based on type transformations for generating
correct-by-construction design variants, and an associated
light-weight cost model for evaluating these variants for
implementation on FPGAs. In this paper we present a key
enabler of our approach, the cost model. We discuss how we
are able to quickly derive accurate estimates of performance
and resource-utilization from the design’s representation in our
intermediate language. We show results confirming the accuracy
of our cost model by testing it on three different scientific
kernels. We conclude with a case-study that compares a solution
generated by our framework with one from a conventional
high-level synthesis tool, showing better performance and
power-efficiency using our cost model based approach
DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge
The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for
processing large astronomical datasets at a scale required by the Square
Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex
data reduction pipelines consisting of both data sets and algorithmic
components and an implementation run-time to execute such pipelines on
distributed resources. By mapping the logical view of a pipeline to its
physical realisation, DALiuGE separates the concerns of multiple stakeholders,
allowing them to collectively optimise large-scale data processing solutions in
a coherent manner. The execution in DALiuGE is data-activated, where each
individual data item autonomously triggers the processing on itself. Such
decentralisation also makes the execution framework very scalable and flexible,
supporting pipeline sizes ranging from less than ten tasks running on a laptop
to tens of millions of concurrent tasks on the second fastest supercomputer in
the world. DALiuGE has been used in production for reducing interferometry data
sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide
Spectral Radioheliograph; and is being developed as the execution framework
prototype for the Science Data Processor (SDP) consortium of the Square
Kilometre Array (SKA) telescope. This paper presents a technical overview of
DALiuGE and discusses case studies from the CHILES and MUSER projects that use
DALiuGE to execute production pipelines. In a companion paper, we provide
in-depth analysis of DALiuGE's scalability to very large numbers of tasks on
two supercomputing facilities.Comment: 31 pages, 12 figures, currently under review by Astronomy and
Computin
- …