pinecone/core-2020-05-10-deduplication huggingface dataset:
Exploring performance and power properties of modern multicore chips via
  simple machine models

Modern multicore chips show complex behavior with respect to performance and
power. Starting with the Intel Sandy Bridge processor, it has become possible
to directly measure the power dissipation of a CPU chip and correlate this data
with the performance properties of the running code. Going beyond a simple
bottleneck analysis, we employ the recently published Execution-Cache-Memory
(ECM) model to describe the single- and multi-core performance of streaming
kernels. The model refines the well-known roofline model, since it can predict
the scaling and the saturation behavior of bandwidth-limited loop kernels on a
multicore chip. The saturation point is especially relevant for considerations
of energy consumption. From power dissipation measurements of benchmark
programs with vastly different requirements to the hardware, we derive a
simple, phenomenological power model for the Sandy Bridge processor. Together
with the ECM model, we are able to explain many peculiarities in the
performance and power behavior of multicore processors, and derive guidelines
for energy-efficient execution of parallel programs. Finally, we show that the
ECM and power models can be successfully used to describe the scaling and power
behavior of a lattice-Boltzmann flow solver code.Comment: 23 pages, 10 figures. Typos corrected, DOI adde
H-SOFT: a heuristic storage space optimisation algorithm for flow table of OpenFlow

PublishedThis is the peer reviewed version of the article, which has been published in final form at DOI 10.1002/cpe.3206. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.OpenFlow has become the key standard and technology for software defined networking, which has been widely adopted in various environments. However, the global deployment of OpenFlow encountered several issues, such as the increasing number of fields and complex structure of flow entries, making the size of flow table in OpenFlow switches explosively grows, which results in hardware implementation difficulty. To this end, this paper presents the modelling on the minimisation for storage space of flow table and proposes a Heuristic Storage space Optimisation algorithm for Flow Table (H-SOFT) to solve this optimisation problem. The H-SOFT algorithm degrades the complex and high-dimensional fields of a flow table into multiple flow tables with simple and low-dimensional fields based on the coexistence and conflict relationships among fields to release the unused storage space due to blank fields. Extensive simulation experiments demonstrate that the H-SOFT algorithm can effectively reduce the storage space of flow table. In particular, with frequent updates on flow entries, the storage space compression rate of flow table is stable and can achieve at ~70%. Moreover, in comparison with the optimal solution, the H-SOFT algorithm can achieve the similar compression rate with much lower execution time.National Natural Science Foundation of ChinaNational Program on Key Basic Research Project (973 Program)Strategic Priority Research Program of the Chinese Academy of SciencesNational High-Tech R&D Program of China (863 Program
TaskLocalRandom: A Statistically Sound Substitute to Pseudorandom Number Generation in Parallel Java Tasks Frameworks

International audienceSeveral software efforts have been produced over the past few years in variousprogramming languages to help developers handle pseudorandom streams partitioning. Paralleland Distributed Stochastic Simulations (PDSS) can obviously benefit from this kind of high-leveltools. The latest release of the Java Development Kit (JDK 7) tries to tackle this problem byproviding facilities to partition a pseudorandom stream across various threads thanks to the newclass ThreadLocalRandom. Meanwhile, Java 7 offers a framework to split a problem in a divide andconquer way through the new class called ForkJoinPool. As any other Java Thread Pool, ForkJoinexploits threads as workers and manipulates the tasks that will be run on the workers. In Thread-LocalRandom, pseudorandom number generation is handled at a thread level. As a consequence,a scientific application taking advantage of a Java Thread Pool to parallelize its computation maysuffer from a bad pseudorandom stream partitioning due to the behaviour of ThreadLocalRandom.The present work introduces TaskLocalRandom, a task-level alternative to ThreadLocalRandomthat solves this partitioning problem and assigns an independent pseudorandom stream to eachtask run in the thread pool. TaskLocalRandom is compatible with existing Java thread pools suchas Executors or ForkJoin
Supporting biodiversity studies with the EUBrazilOpenBio hybrid data infrastructure

EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user-friendly tools widely available in Brazil and Europe. The project deploys the EU-Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on-demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross-mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and experimental results.EUBrazilOpenBio - Open Data and Cloud Computing e-Infrastructure for Biodiversity (2011-2013) is a Small or medium-scale focused research project (STREP) funded by\ud
the European Commission under the Cooperation Programme,\ud
Framework Programme Seven (FP7) Objective FP7-ICT-2011-EU-Brazil Research and Development cooperation, and the\ud
National Council for Scientific and Technological Development of Brazil (CNPq) of the Brazilian Ministry of Science, Technology and Innovation (MCTI) under the corresponding\ud
matching Brazilian Call for proposals MCT/CNPq 066/2010.Peer ReviewedPostprint (author’s final draft
A three step blind approach for improving HPC systems' energy performance

Nowadays, there is no doubt that energy consumption has become a limiting factor in the design and operation of high performance computing (HPC) systems. This is evidenced by the rise of efforts both from the academia and the industry to reduce the energy consumption of those systems. Unlike hardware solutions, software initiatives targeting HPC systems’ energy consumption reduction despite their effectiveness are often limited for reasons including: (1) the program specific nature of the solution proposed; (2) the need of deep understanding of applications at hand; (3) proposed solutions are often difficult to use by novices and/or are designed for single task environments. This paper propose a three step blind system-wide, application independent, fine-grain, and easy to use (user friendly) methodology for improving energy performance of HPC systems. The ethodology typically breaks into phase detection, phase characterization, and phase identifycation and system reconfiguration. And it is blind in the sense that it does not require any knowledge from users. It relies upon reconfigurable capabilities offered by the majority of HPC subsystems – including the processor, storage, memory, and communication subsystems – to reduce the overall energy consumption of the system (excluding network equipments) at runtime. We also present an implementation of our methodology through which we demonstrate its effectiveness via static analyses and experiments using benchmarks representative of HPC workloads
Insights into the issue in IPv6 adoption: a view from the Chinese IPv6 Application mix

Published onlineThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Although IPv6 has been standardized more than 15 years ago, its deployment is still very limited. China has been strongly pushing IPv6, especially due to its limited IPv4 address space. In this paper, we describe measurements from a large Chinese academic network, serving a significant population of IPv6 hosts. We show that despite its expected strength, China is struggling as much as the western world to increase the share of IPv6 traffic. To understand the reasons behind this, we examine the IPv6 applicative ecosystem. We observe a significant IPv6 traffic growth over the past 3 years, with P2P file transfers responsible for more than 80% of the IPv6 traffic, compared with only 15% for IPv4 traffic. Checking the top websites for IPv6 explains the dominance of P2P, with popular P2P trackers appearing systematically among the top visited sites, followed by Chinese popular services (e.g., Tencent), as well as surprisingly popular third-party analytics including Google. Finally, we compare the throughput of IPv6 and IPv4 flows. We find that a larger share of IPv4 flows get a high-throughput compared with IPv6 flows, despite IPv6 traffic not being rate limited. We explain this through the limited amount of HTTP traffic in IPv6 and the presence of Web caches in IPv4. Our findings highlight the main issue in IPv6 adoption, that is, the lack of commercial content, which biases the geographic pattern and flow throughput of IPv6 traffic. Copyright © 2014 John Wiley & Sons, Ltd
An effective single-hop distributed hash table with high lookup
  performance and low traffic overhead

Distributed Hash Tables (DHTs) have been used in several applications, but
most DHTs have opted to solve lookups with multiple hops, to minimize bandwidth
costs while sacrificing lookup latency. This paper presents D1HT, an original
DHT which has a peer-to-peer and self-organizing architecture and maximizes
lookup performance with reasonable maintenance traffic, and a Quarantine
mechanism to reduce overheads caused by volatile peers. We implemented both
D1HT and a prominent single-hop DHT, and we performed an extensive and highly
representative DHT experimental comparison, followed by complementary
analytical studies. In comparison with current single-hop DHTs, our results
showed that D1HT consistently had the lowest bandwidth requirements, with
typical reductions of up to one order of magnitude, and that D1HT could be used
even in popular Internet applications with millions of users. In addition, we
ran the first latency experiments comparing DHTs to directory servers, which
revealed that D1HT can achieve latencies equivalent to or better than a
directory server, and confirmed its greater scalability properties. Overall,
our extensive set of results allowed us to conclude that D1HT can provide a
very effective solution for a broad range of environments, from large-scale
corporate datacenters to widely deployed Internet applications.Comment: This is the pre-peer reviewed version of the following article: Luiz
  Monnerat and Claudio L. Amorim, An effective single-hop distributed hash
  table with high lookup performance and low traffic overhead, Concurrency and
  Computation: Practice and Experience (CCPE), 2014, which has been published
  in final form at http://onlinelibrary.wiley.com/doi/10.1002/cpe.3342/abstrac
Out-of-core macromolecular simulations on multithreaded architectures

We address the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on multithreaded platforms, consisting of multicore processors and possibly a graphics processor (GPU). In particular, we compare specialized implementations of several high- performance eigensolvers that, by relying on disk storage and out-of-core (OOC) techniques, can in principle tackle the large memory requirements of these biological problems, which in general do not fit into the main memory of current desktop machines. All these OOC eigensolvers, except for one, are composed of compute-bound (i.e., arithmetically-intensive) operations, which we accelerate by exploiting the performance of current multicore processors and, in some cases, by additionally off-loading certain parts of the computation to a GPU accelerator. One of the eigensolvers is a memory-bound algorithm, which strongly constrains its performance when the data is on disk. However, this method exhibits a much lower arithmetic cost compared with its compute- bound alternatives for this particular application. Experimental results on a desktop platform, representative of current server technology, illustrate the potential of these methods to address the simulation of biological activity
Scheduling independent tasks on multi-cores with GPU accelerators

International audienceMore and more computers use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphics Process-ing Units). We present in this paper a new method for scheduling efficiently parallel applications with m CPUs and k GPUs, where each task of the appli-cation can be processed either on a core (CPU) or on a GPU. The objective is to minimize the maximum completion time (makespan). The corresponding scheduling problem is NP-hard, we propose an efficient approximation algo-rithm which achieves an approximation ratio of 4 3 + 1 3k . We first detail and analyze the method, based on a dual approximation scheme, that uses dynamic programming to balance evenly the load between the heterogeneous resources. Then, we present a faster approximation algorithm for a special case of the previous problem, where all the tasks are accelerated when affected to GPU, with a performance guarantee of 3 2 for any number of GPUs. We run some simulations based on realistic benchmarks and compare the solutions obtained by a relaxed version of the generic method to the one provided by a classical scheduling algorithm (HEFT). Finally, we present an implementation of the 4/3-approximation and its relaxed version on a classical linear algebra kernel into the scheduler of the xKaapi runtime system
A massive data processing approach for effective trustworthiness in online learning groups

This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.htmlThis paper proposes a trustworthiness-based approach for the design of secure learning activities in online learning groups. Although computer-supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks that limit its potential. Among these limitations, we investigate on information security vulnerabilities in learning activities, which may be developed in online collaborative learning contexts. Although security advanced methodologies and technologies are deployed in learning management systems, many security vulnerabilities are still not satisfactorily solved. To overcome these deficiencies, we first propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness approach. However, as learners' trustworthiness analysis involves large amount of data generated along learning activities, processing this information is computationally costly, especially if required in real time. As the main contribution of this paper, we eventually propose a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time.Peer ReviewedPostprint (author's final draft
A Scalable Approach to Solving Dense Linear Algebra Problems on Hybrid CPU-GPU Systems

Aiming to fully exploit the computing power of all CPUs and all graphics processing units (GPUs) on hybrid CPU-GPU systems to solve dense linear algebra problems, we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, and to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentralized dynamic scheduling runtime system, which schedules a task graph dynamically and transfers data between compute nodes automatically. The runtime system uses a new distributed task assignment protocol to solve data dependencies between tasks without any coordination between processing units. By overlapping computation and communication through dynamic scheduling, we are able to attain scalable performance for the double-precision Cholesky factorization and QR factorization. Our approach demonstrates a performance comparable to Intel MKL on shared-memory multicore systems and better performance than both vendor (e.g., Intel MKL) and open source libraries (e.g., StarPU) in the following three environments: heterogeneous clusters with GPUs, conventional clusters without GPUs, and shared-memory systems with multiple GPUs
HybridMR: a New Approach for Hybrid MapReduce Combining Desktop Grid and Cloud Infrastructures

International audienceThis paper introduces HybridMR, a novel model for the execution of MapReduce (MR) computation on hybrid computing environment. Using this model, high performance cloud resources and heterogeneous desktop personal computers (PCs) in Internet or Intranet can be integrated to form a hybrid computing environment. Thanks to HybridMR, the computation and storage capability of large scale desktop PCs can be fully utilized to process large scale datasets. HybridMR relies on two innovative solutions to enable such large scale data-intensive computation. The first one is HybridDFS, which is a hybrid distributed file system. HybridDFS features reliable distributed storage that alleviates the volatility of desktop PCs, thanks to fault tolerance and file replication mechanism. The second innovation is a new node priority-based fair scheduling (NPBFS) algorithm has been developed in HybridMR to achieve both data storage balance and job assignment balance by assigning each node a priority through quantifying CPU speed, memory size, and input and output capacity. In this paper, we describe the HybridMR, HybridDFS, and NPBFS. We report on performance evaluation results, which show that the proposed HybridMR not only achieves reliable MR computation, reduces task response time, and improves the performance of MR, but also reduces the computation cost and achieves a greener computing mode
Science Gateways Institute Survey

This deposit includes (1) the invitation and reminder messages sent to the survey sample and (2) the survey questions, including instructions for branching.A paper describing the results of this survey will be part of a special issue of the journal CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE, published by Wiley.Science gateways are digital interfaces to advanced technologies that support science/engineering research/education. Frequently implemented as web and mobile applications, they provide access to community resources such as software, data, collaboration tools, instrumentation, and high-performance computing. Through a large-scale survey, we measured the extent and characteristics of the gateway community (reliance on gateways, nature of existing resources) to understand useful services and support for builders and users. This survey was designed to be administered to principal investigators, senior administrators, and people with gateway affiliations. The survey has three main sections of questions for respondents who self-identify as administrators, application creators, or researchers/educators. An additional set of questions is administered to all respondents
Spectral turning bands for efficient Gaussian random fields generation on GPUs and accelerators

A random field (RF) is a set of correlated random variables associated with different spatial locations. RF generation algorithms are of crucial importance for many scientific areas, such as astrophysics, geostatistics, computer graphics, and many others. Current approaches commonly make use of 3D fast Fourier transform (FFT), which does not scale well for RF bigger than the available memory; they are also limited to regular rectilinear meshes.
We introduce random field generation with the turning band method (RAFT), an RF generation algorithm based on the turning band method that is optimized for massively parallel hardware such as GPUs and accelerators. Our algorithm replaces the 3D FFT with a lower‐order, one‐dimensional FFT followed by a projection step and is further optimized with loop unrolling and blocking. RAFT can easily generate RF on non‐regular (non‐uniform) meshes and efficiently produce fields with mesh sizes bigger than the available device memory by using a streaming, out‐of‐core approach. Our algorithm generates RF with the correct statistical behavior and is tested on a variety of modern hardware, such as NVIDIA Tesla, AMD FirePro and Intel Phi. RAFT is faster than the traditional methods on regular meshes and has been successfully applied to two real case scenarios: planetary nebulae and cosmological simulations
The DEEP Project An alternative approach to heterogeneous cluster-computing in the many-core era

Homogeneous cluster architectures, which used to dominate high-performance computing (HPC), are challenged today by heterogeneous approaches utilizing accelerator or co-processor devices. The DEEP (Dynamical Exascale Entry Platform) project is implementing a novel architecture for HPC, in which a standard HPC cluster is directly connected to a so-called ‘Booster’: a cluster of many-core processors. By these means heterogeneity is organized differently as in today's standard approach, where accelerators are added to each node of the cluster. In order to adapt application codes to this Cluster-Booster architecture as seamless as possible, DEEP has developed a complete programming environment. It integrates the offloading functionality given by the Message Passing Interface standard with an abstraction layer based on the task-based OmpSs programming paradigm. This paper presents the DEEP project with an emphasis on the DEEP programming environment
Execution of Compound Multi-Kernel OpenCL Computations in
  Multi-CPU/Multi-GPU Environments

Current computational systems are heterogeneous by nature, featuring a
combination of CPUs and GPUs. As the latter are becoming an established
platform for high-performance computing, the focus is shifting towards the
seamless programming of these hybrid systems as a whole. The distinct nature of
the architectural and execution models in place raises several challenges, as
the best hardware configuration is behaviour and workload dependent. In this
paper, we address the execution of compound, multi-kernel, OpenCL computations
in multi-CPU/multi-GPU environments. We address how these computations may be
efficiently scheduled onto the target hardware, and how the system may adapt
itself to changes in the workload to process and to fluctuations in the CPU's
load. An experimental evaluation attests the performance gains obtained by the
conjoined use of the CPU and GPU devices, when compared to GPU-only executions,
and also by the use of data-locality optimizations in CPU environments.Comment: in Concurrency Computat.: Pract. Exper., 201