12,169 research outputs found
REPP-H: runtime estimation of power and performance on heterogeneous data centers
Modern data centers increasingly demand improved performance with minimal power consumption. Managing the power and performance requirements of the applications is challenging because these data centers, incidentally or intentionally, have to deal with server architecture heterogeneity [19], [22]. One critical challenge that data centers have to face is how to manage system power and performance given the different application behavior across multiple different architectures.This work has been supported by the EU FP7 program (Mont-Blanc 2, ICT-610402), by the
Ministerio de Economia (CAP-VII, TIN2015-65316-P), and the Generalitat de Catalunya (MPEXPAR, 2014-SGR-1051).
The material herein is based in part upon work supported by the US NSF, grant numbers ACI-1535232 and CNS-1305220.Peer ReviewedPostprint (author's final draft
ALOJA: A benchmarking and predictive platform for big data performance analysis
The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1.
This article describes the evolution of the project's focus and research
lines from over a year of continuously benchmarking Hadoop under dif-
ferent configuration and deployments options, presents results, and dis
cusses the motivation both technical and market-based of such changes.
During this time, ALOJA's target has evolved from a previous low-level
profiling of Hadoop runtime, passing through extensive benchmarking
and evaluation of a large body of results via aggregation, to currently
leveraging Predictive Analytics (PA) techniques. Modeling benchmark
executions allow us to estimate the results of new or untested configu-
rations or hardware set-ups automatically, by learning techniques from
past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span-
ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
Exploring Application Performance on Emerging Hybrid-Memory Supercomputers
Next-generation supercomputers will feature more hierarchical and
heterogeneous memory systems with different memory technologies working
side-by-side. A critical question is whether at large scale existing HPC
applications and emerging data-analytics workloads will have performance
improvement or degradation on these systems. We propose a systematic and fair
methodology to identify the trend of application performance on emerging
hybrid-memory systems. We model the memory system of next-generation
supercomputers as a combination of "fast" and "slow" memories. We then analyze
performance and dynamic execution characteristics of a variety of workloads,
from traditional scientific applications to emerging data analytics to compare
traditional and hybrid-memory systems. Our results show that data analytics
applications can clearly benefit from the new system design, especially at
large scale. Moreover, hybrid-memory systems do not penalize traditional
scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and
Communications, IEEE, 201
Racing to hardware-validated simulation
Processor simulators rely on detailed timing models of the processor pipeline to evaluate performance. The diversity in real-world processor designs mandates building flexible simulators that expose parts of the underlying model to the user in the form of configurable parameters. Consequently, the accuracy of modeling a real processor relies on both the accuracy of the pipeline model itself, and the accuracy of adjusting the configuration parameters according to the modeled processor. Unfortunately, processor vendors publicly disclose only a subset of their design decisions, raising the probability of introducing specification inaccuracies when modeling these processors. Inaccurately tuning model parameters deviates the simulated processor from the actual one. In the worst case, using improper parameters may lead to imbalanced pipeline models compromising the simulation output. Therefore, simulation models should be hardware-validated before using them for performance evaluation. As processors increase in complexity and diversity, validating a simulator model against real hardware becomes increasingly more challenging and time-consuming. In this work, we propose a methodology for validating simulation models against real hardware. We create a framework that relies on micro-benchmarks to collect performance statistics on real hardware, and machine learning-based algorithms to fine-tune the unknown parameters based on the accumulated statistics. We overhaul the Sniper simulator to support the ARM AArch64 instruction-set architecture (ISA), and introduce two new timing models for ARM-based in-order and out-of-order cores. Using our proposed simulator validation framework, we tune the in-order and out-of-order models to match the performance of a real-world implementation of the Cortex-A53 and Cortex-A72 cores with an average error of 7% and 15%, respectively, across a set of SPEC CPU2017 benchmarks
The state of SQL-on-Hadoop in the cloud
Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud,
and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark.
The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines.
The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization.
The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some
providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under
the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat
de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments
This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Unionâs Horizon 2020 research and innovation programme (grant agreement
No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version
An Assessment to Benchmark the Seismic Performance of a Code-Conforming Reinforced-Concrete Moment-Frame Building
This report describes a state-of-the-art performance-based earthquake engineering methodology
that is used to assess the seismic performance of a four-story reinforced concrete (RC) office
building that is generally representative of low-rise office buildings constructed in highly seismic
regions of California. This âbenchmarkâ building is considered to be located at a site in the Los
Angeles basin, and it was designed with a ductile RC special moment-resisting frame as its
seismic lateral system that was designed according to modern building codes and standards. The
buildingâs performance is quantified in terms of structural behavior up to collapse, structural and
nonstructural damage and associated repair costs, and the risk of fatalities and their associated
economic costs. To account for different building configurations that may be designed in
practice to meet requirements of building size and use, eight structural design alternatives are
used in the performance assessments.
Our performance assessments account for important sources of uncertainty in the ground
motion hazard, the structural response, structural and nonstructural damage, repair costs, and
life-safety risk. The ground motion hazard characterization employs a site-specific probabilistic
seismic hazard analysis and the evaluation of controlling seismic sources (through
disaggregation) at seven ground motion levels (encompassing return periods ranging from 7 to
2475 years). Innovative procedures for ground motion selection and scaling are used to develop
acceleration time history suites corresponding to each of the seven ground motion levels.
Structural modeling utilizes both âfiberâ models and âplastic hingeâ models. Structural
modeling uncertainties are investigated through comparison of these two modeling approaches,
and through variations in structural component modeling parameters (stiffness, deformation
capacity, degradation, etc.). Structural and nonstructural damage (fragility) models are based on
a combination of test data, observations from post-earthquake reconnaissance, and expert
opinion. Structural damage and repair costs are modeled for the RC beams, columns, and slabcolumn connections. Damage and associated repair costs are considered for some nonstructural
building components, including wallboard partitions, interior paint, exterior glazing, ceilings,
sprinkler systems, and elevators. The risk of casualties and the associated economic costs are
evaluated based on the risk of structural collapse, combined with recent models on earthquake
fatalities in collapsed buildings and accepted economic modeling guidelines for the value of
human life in loss and cost-benefit studies.
The principal results of this work pertain to the building collapse risk, damage and repair
cost, and life-safety risk. These are discussed successively as follows.
When accounting for uncertainties in structural modeling and record-to-record variability
(i.e., conditional on a specified ground shaking intensity), the structural collapse probabilities of
the various designs range from 2% to 7% for earthquake ground motions that have a 2%
probability of exceedance in 50 years (2475 years return period). When integrated with the
ground motion hazard for the southern California site, the collapse probabilities result in mean
annual frequencies of collapse in the range of [0.4 to 1.4]x10
-4
for the various benchmark
building designs. In the development of these results, we made the following observations that
are expected to be broadly applicable:
(1) The ground motions selected for performance simulations must consider spectral
shape (e.g., through use of the epsilon parameter) and should appropriately account for
correlations between motions in both horizontal directions;
(2) Lower-bound component models, which are commonly used in performance-based
assessment procedures such as FEMA 356, can significantly bias collapse analysis results; it is
more appropriate to use median component behavior, including all aspects of the component
model (strength, stiffness, deformation capacity, cyclic deterioration, etc.);
(3) Structural modeling uncertainties related to component deformation capacity and
post-peak degrading stiffness can impact the variability of calculated collapse probabilities and
mean annual rates to a similar degree as record-to-record variability of ground motions.
Therefore, including the effects of such structural modeling uncertainties significantly increases
the mean annual collapse rates. We found this increase to be roughly four to eight times relative
to rates evaluated for the median structural model;
(4) Nonlinear response analyses revealed at least six distinct collapse mechanisms, the
most common of which was a story mechanism in the third story (differing from the multi-story
mechanism predicted by nonlinear static pushover analysis);
(5) Soil-foundation-structure interaction effects did not significantly affect the structural
response, which was expected given the relatively flexible superstructure and stiff soils.
The potential for financial loss is considerable. Overall, the calculated expected annual
losses (EAL) are in the range of 97,000 for the various code-conforming benchmark
building designs, or roughly 1% of the replacement cost of the building (3.5M, the fatality rate translates to an EAL due to
fatalities of 5,600 for the code-conforming designs, and 66,000, the monetary value associated with life loss is small,
suggesting that the governing factor in this respect will be the maximum permissible life-safety
risk deemed by the public (or its representative government) to be appropriate for buildings.
Although the focus of this report is on one specific building, it can be used as a reference
for other types of structures. This report is organized in such a way that the individual core
chapters (4, 5, and 6) can be read independently. Chapter 1 provides background on the
performance-based earthquake engineering (PBEE) approach. Chapter 2 presents the
implementation of the PBEE methodology of the PEER framework, as applied to the benchmark
building. Chapter 3 sets the stage for the choices of location and basic structural design. The subsequent core chapters focus on the hazard analysis (Chapter 4), the structural analysis
(Chapter 5), and the damage and loss analyses (Chapter 6). Although the report is self-contained,
readers interested in additional details can find them in the appendices
Validating a timing simulator for the NGMP multicore processor
Timing simulation is a key element in multicore systems design. It enables a fast and cost effective design space exploration, allowing to simulate new architectural improvements without requiring RTL abstraction levels. Timing simulation also allows software developers to perform early testing of the timing behavior of their software without the need of buying the actual physical board, which can be very expensive when the board uses non-COTS technology. In this paper we present the validation of a timing simulator for the NGMP multicore processor, which is a 4 core processor being developed to become the reference platform for future missions of the European Space Agency.The research leading to these results has received funding from the European Space Agency under contract NPI 4000102880 and the Ministry of Science and Technology of
Spain under contract TIN-2015-65316-P. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship
number RYC-2013-14717.Peer ReviewedPostprint (author's final draft
- âŠ