pinecone/core-2020-05-10-deduplication huggingface dataset:
Design methodology for workload-aware loop scheduling strategies based on genetic algorithm and simulation

International audienceIn high-performance computing, the application's workload must be evenly balanced among threads to deliver cutting-edge performance and scalability. In OpenMP, the load balancing problem arises when scheduling loop iterations to threads. In this context, several scheduling strategies have been proposed, but they do not take into account the input workload of the application and thus turn out to be suboptimal. In this work, we introduce a design methodology to propose, study, and assess the performance of workload-aware loop scheduling strategies. In this methodology, a genetic algorithm is employed to explore the state space solution of the problem itself and to guide the design of new loop scheduling strategies, and a simulator is used to evaluate their performance. As a proof of concept, we show how the proposed methodology was used to propose and study a new workload-aware loop scheduling strategy named smart round-robin (SRR). We implemented this strategy into GNU Compiler Collection's OpenMP runtime. We carry out several experiments to validate the simulator and to evaluate the performance of SRR. Our experimental results show that SRR may deliver up to 37.89% and 14.10% better performance than OpenMP's dynamic loop scheduling strategy in the simulated environment and in a real-world application kernel, respectively
Code Synthesis to Optimize Accuracy and Execution Time of Floating-Point Programs

The definitive version of this preliminary work is available here https://hal.archives-ouvertes.fr/hal-01236919v2 or here https://doi.org/10.1002/cpe.3953.We present the SyHD software developed to perform source-to-source transformation improving accuracy without impacting execution time too much. SyHD synthesizes C source code for both accuracy and execution-time criteria. It uses compensation for improving accuracy and transformation strategies for reducing the impact of this improvement on execution time
Coalition structure generation problems : optimization and parallelization of the IDP algorithm in multicore systems

The coalition structure generation problem is well known in the area of multi-agent systems. Its goal is to establish coalitions between agents while maximizing the global welfare. Among the existing different algorithms designed to solve the coalition structure generation problem, DP and IDP are the ones with smaller temporal complexity. After analyzing the operation of the dynamic programming and improved dynamic programming algorithms, we have identified which are the most frequent operations and propose an optimized method. In addition, we study and implement a method for dividing the work into different threads. To describe incremental improvements of the algorithm design, we first compare performance of an improved single central processing unit core version where we obtain speedups ranging from 7 × to 11 × . Then, we describe the best resource use in a multi-thread optimized version where we obtain an additional 7.5 × speedup running in a 12-core machin
Efficient quality of service‐aware packet chunking scheme for machine‐to‐machine cloud services

With the recent advances in machine-to-machine(M2M) communications, huge numbers of devices have become connected and massive amounts of traffic are exchanged. M2M applications typically generate small packets, which can profoundly affect the network performance. Namely, even if the packet arrival rate at the router is lower than the link bandwidth, bits per second(BPS), it can exceed the router forwarding capacity, which indicates the maximum number of forwarded packets per second(PPS). This will cause the decrease in the network throughput. Therefore, eliminating the PPS limitation by chunking small packets will enable M2M cloud services to spread further. This paper proposes new packet-chunking schemes aimed at meeting both application requirements and improving achievable router throughput. In our schemes, multiple buffers, each of which accommodates packets classified based on their delay requirement, are installed in parallel. Herein, we report on analysis of the theoretically performance of these schemes, which enabled us to derive some important features. We also propose a scheme whereby a single chunking buffer and parallel multiple buffers were arranged in tandem. Through our simulation and numerical results, we determined that these schemes provide excellent performance in reducing the number of outgoing packets from the router while meeting various delay requirements.The 2nd IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (IEEE HPCA 2016),March 12, 2016, Barcelona, Spai
On the benefits of the remote GPU virtualization mechanism: The rCUDA case

Graphics processing units (GPUs) are being adopted in many computing facilities given their extraordinary computing power, which makes it possible to accelerate many general purpose applications from different domains. However, GPUs also present several side effects, such as increased acquisition costs as well as larger space requirements. They also require more powerful energy supplies. Furthermore, GPUs still consume some amount of energy while idle, and their utilization is usually low for most workloads. In a similar way to virtual machines, the use of virtual GPUs may address the aforementioned concerns. In this regard, the remote GPU virtualization mechanism allows an application being executed in a node of the cluster to transparently use the GPUs installed at other nodes. Moreover, this technique allows to share the GPUs present in the computing facility among the applications being executed in the cluster. In this way, several applications being executed in different (or the same) cluster nodes can share 1 or more GPUs located in other nodes of the cluster. Sharing GPUs should increase overall GPU utilization, thus reducing the negative impact of the side effects mentioned before. Reducing the total amount of GPUs installed in the cluster may also be possible. In this paper, we explore some of the benefits that remote GPU virtualization brings to clusters. For instance, this mechanism allows an application to use all the GPUs present in the computing facility. Another benefit of this technique is that cluster throughput, measured as jobs completed per time unit, is noticeably increased when this technique is used. In this regard, cluster throughput can be doubled for some workloads. Furthermore, in addition to increase overall GPU utilization, total energy consumption can be reduced up to 40%. This may be key in the context of exascale computing facilities, which present an important energy constraint. Other benefits are related to the cloud computing domain, where a GPU can be easily shared among several virtual machines. Finally, GPU migration (and therefore server consolidation) is one more benefit of this novel technique
Energy Optimization Methodology for e-Infrastructure Providers

The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e-infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e-infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e-infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e-infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission
A Survey on Load Balancing Algorithms for VM Placement in Cloud
  Computing

The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Evaluation of DVFS techniques on modern HPC processors and accelerators
  for energy-aware applications

Energy efficiency is becoming increasingly important for computing systems,
in particular for large scale HPC facilities. In this work we evaluate, from an
user perspective, the use of Dynamic Voltage and Frequency Scaling (DVFS)
techniques, assisted by the power and energy monitoring capabilities of modern
processors in order to tune applications for energy efficiency. We run selected
kernels and a full HPC application on two high-end processors widely used in
the HPC context, namely an NVIDIA K80 GPU and an Intel Haswell CPU. We evaluate
the available trade-offs between energy-to-solution and time-to-solution,
attempting a function-by-function frequency tuning. We finally estimate the
benefits obtainable running the full code on a HPC multi-GPU node, with respect
to default clock frequency governors. We instrument our code to accurately
monitor power consumption and execution time without the need of any additional
hardware, and we enable it to change CPUs and GPUs clock frequencies while
running. We analyze our results on the different architectures using a simple
energy-performance model, and derive a number of energy saving strategies which
can be easily adopted on recent high-end HPC systems for generic applications
A scalable synthetic traffic model of Graph500 for computer networks analysis

The Graph500 benchmark attempts to steer the design of High-Performance Computing systems to maximize the performance under memory-constricted application workloads. A realistic simulation of such benchmarks for architectural research is challenging due to size and detail limitations. By contrast, synthetic traffic workloads constitute one of the least resource-consuming methods to evaluate the performance. In this work, we provide a simulation tool for network architects that need to evaluate the suitability of their interconnect for BigData applications. Our development is a low computation- and memory-demanding synthetic traffic model that emulates the behavior of the Graph500 communications and is publicly available in an open-source network simulator. The characterization of network traffic is inferred from a profile of several executions of the benchmark with different input parameters. We verify the validity of the equations in our model against an execution of the benchmark with a different set of parameters. Furthermore, we identify the impact of the node computation capabilities and network characteristics in the execution time of the model in a Dragonfly networkThe authors would like to thank the European HiPEAC Network of Excellence for partially funding this work through a Collaboration Grant. They would like to thank as well Crist´obal Camarero for his help. This work\ud
has been supported by the Spanish Ministry of Education, FPU grants FPU13/00337 and FPU14/02253, the Spanish Ministry of Economy, Industry and Competitiveness under contract TIN2016-76635-C2-2-R (AEI/FEDER, UE), and by the Mont-Blanc project. The Mont-Blanc project has received funding\ud
from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671697. Santander Supercomputacion support group from the University of Cantabria provided access to\ud
the Altamira Supercomputer at the Institute of Physics of Cantabria (IFCA-CSIC).Peer ReviewedPostprint (author's final draft
RIoTBench: A Real-time IoT Benchmark for Distributed Stream Processing
  Platforms

The Internet of Things (IoT) is an emerging technology paradigm where
millions of sensors and actuators help monitor and manage, physical,
environmental and human systems in real-time. The inherent closedloop
responsiveness and decision making of IoT applications make them ideal
candidates for using low latency and scalable stream processing platforms.
Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are
becoming the vital engine for real-time data processing and analytics in any
IoT software architecture. But the efficacy and performance of contemporary
DSPS have not been rigorously studied for IoT applications and data streams.
Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with
performance metrics, to evaluate DSPS for streaming IoT applications. The
benchmark includes 27 common IoT tasks classified across various functional
categories and implemented as reusable micro-benchmarks. Further, we propose
four IoT application benchmarks composed from these tasks, and that leverage
various dataflow semantics of DSPS. The applications are based on common IoT
patterns for data pre-processing, statistical summarization and predictive
analytics. These are coupled with four stream workloads sourced from real IoT
observations on smart cities and fitness, with peak streams rates that range
from 500 to 10000 messages/sec and diverse frequency distributions. We validate
the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure
public Cloud, and present empirical observations. This suite can be used by
DSPS researchers for performance analysis and resource scheduling, and by IoT
practitioners to evaluate DSPS platforms.Comment: 33 pages. arXiv admin note: substantial text overlap with
  arXiv:1606.0762
Performance formula-based optimal deployments of multilevel indices for service retrieval.

There are many different index structures for service repositories, such as sequential index, inverted index, and multilevel indices that include three deployments. Different service sets maybe have different characteristics that may affect performance from different aspects. For a given service set, which index structure is the most optimal one? To address these issues, this paper analyses five indexing models and proposes expectation of traversed service count to estimate performance of service retrieval. Based on these expectation formulas, an optimal deployment method can be identified to maximize efficiency of service retrieval. Our experiments first validate correctness of the proposed formulas and then validate the effective of the optimal method
Distributed Hybrid Simulation of the Internet of Things and Smart
  Territories

This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
A multi-core ready discrete element method with triangles using dynamically adaptive multiscale grids.

The simulation of vast numbers of rigid bodies of non‐analytical shapes and of tremendously different sizes that collide with each other is computationally challenging. A bottleneck is the identification of all particle contact points per time step. We propose a tree‐based multilevel meta data structure to administer the particles. The data structure plus a purpose‐made tree traversal identifying the contact points introduce concurrency to the particle comparisons, whilst they keep the absolute number of particle‐to‐particle comparisons low. Furthermore, a novel adaptivity criterion allows explicit time stepping to work with comparably large time steps. It optimises both toward low algorithmic complexity per time step and low numbers of time steps. We study three different parallelisation strategies exploiting our traversal's concurrency. The fusion of two of them yields promising speedups once we rely on maximally asynchronous task‐based realisations. Our work shows that new computer architecture can push the boundary of rigid particle computability, yet if and only if the right data structures and data processing schemes are chosen
BIGhybrid: A Simulator for MapReduce Applications in Hybrid Distributed Infrastructures Validated with the Grid5000 Experimental Platform

International audienceSUMMARY Cloud computing has increasingly been used as a platform for running large business and data processing applications. Conversely, Desktop Grids have been successfully employed in a wide range of projects, because they are able to take advantage of a large number of resources provided free of charge by volunteers. A hybrid infrastructure created from the combination of Cloud and Desktop Grids infrastructures can provide a low-cost and scalable solution for Big Data analysis. Although frameworks like MapReduce have been designed to exploit commodity hardware, their ability to take advantage of a hybrid infrastructure poses significant challenges due to their large resource heterogeneity and high churn rate. In this paper is proposed BIGhybrid, a simulator for two existing classes of MapReduce runtime environments: BitDew-MapReduce designed for Desktop Grids and BlobSeer-Hadoop designed for Cloud computing, where the goal is to carry out accurate simulations of MapReduce executions in a hybrid infrastructure composed of Cloud computing and Desktop Grid resources. This work describes the principles of the simulator and describes the validation of BigHybrid with the Grid5000 experimental platform. Owing to BigHybrid, developers can investigate and evaluate new algorithms to enable MapReduce to be executed in hybrid infrastructures. This includes topics such as resource allocation and data splitting. Concurrency and Computation: Practice and Experienc
Achieving Portable and Efficient Parallel CORBA Objects

International audienceWith the availability of Computational Grids, new kinds of applications are emerging. They raise the problem of how to program them on such computing systems. In this paper, we advocate a programming model based on a combination of parallel and distributed programming models. Compared to previous approaches, this work aims at bringing SPMD programming into CORBA in a portable way. For example, we want to interconnect two parallel codes by CORBA without modifying either CORBA or the parallel communication API. We show that such an approach does not entail any loss of performance compared to previous approaches that required modification to the CORBA standard. Moreover, using an ORB that is able to exploit high performance networks, we show that portable parallel CORBA objects can efficiently make use of such networks
Determining the Gibbs Energy of Ion Transfer Across Water-Organic Liquid Interfaces with Three-Phase Electrodes

Ions can be transferred between immiscible liquid phases across\ud
a common interface, with the help of a three-electrode potentiostat,\ud
when one phase is an organic droplet attached to a solid\ud
electrode and containing a redox probe. This novel approach has\ud
been used in studies to determine the Gibbs energy of anion and\ud
cation transfer, ranging from simple inorganic and organic ions\ud
to the ionic forms of drugs and small peptides. This method of\ud
studying ion transfer has the following advantages: 1) no base\ud
electrolytes are necessary in the organic phase; 2) the aqueous\ud
phase contains only the salt to be studied; 3) a three-electrode\ud
potentiostat is used; 4) organic solvents such as n-octanol and\ud
chiral liquids such as d- and l-2-octanol can be used; 5) the\ud
range of accessible Gibbs energies of transfer is wider than in the\ud
classic 4-electrode experiments; 6) the volume of the organic\ud
phase can be very small, for example, 1 mL or less; 7) the experiments\ud
can be performed routinely and fast. Herein, the basic\ud
principle is outlined, as well as a summary of the results obtained\ud
to date, and a discussion on the theoretical treatments concerning\ud
the kinetic regime of the three-phase electrodes with immobilized\ud
droplets
Structure determination of resorcinol rotamers by high-resolution UV spectroscopy

Contains fulltext :
                  33021.pdf (preprint version ) (Open Access)The rotationally resolved S-1 <- S-0 electronic origins of several deuterated resorcinol rotamers cooled in a molecular beam have been recorded. An automated assignment of the observed spectra has been performed using a genetic algorithm approach with an asymmetric rotor Hamiltonian. The structures of resorcinol A and resorcinol B were derived from the rotational constants of twenty deuterated species for both electronic states. The lifetimes of different resorcinol isotopomers in the S-1 state are also reported. As is the case for phenol, these lifetimes mainly depend on the position of deuteration. A nearly perfect additivity of the zero-point energies after successive deuterations in resorcinol rotamers has been discovered and subsequently used in the full assignment of the previously reported low-resolution spectra of deuterated resorcinol A. An analogous spectrum is also predicted for the resorcinol 8 rotamer