On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case

[EN] 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.Generalitat Valenciana, Grant/Award Number: PROMETEOII/2013/009; MINECO and FEDER, Grant/Award Number: TIN2014-53495-RSilla Jiménez, F.; Iserte Agut, S.; Reaño González, C.; Prades, J. (2017). On the Benefits of the Remote GPU Virtualization Mechanism: the rCUDA Case. 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A complete and efficient CUDA-sharing solution for HPC clusters

In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than nodes, as well as permits a single node to exploit the whole set of GPUs installed in the cluster. In our proposal, CUDA applications can seamlessly interact with any GPU in the cluster, independently of its physical location. Thus, GPUs can be either distributed among compute nodes or concentrated in dedicated GPGPU servers, depending on the cluster administrator’s policy. This proposal leads to savings not only in space but also in energy, acquisition, and maintenance costs. The performance evaluation in this paper with a series of benchmarks and a production application clearly demonstrates the viability of this proposal. Concretely, experiments with the matrix–matrix product reveal excellent performance compared with regular executions on the local GPU; on a much more complex application, the GPU-accelerated LAMMPS, we attain up to 11x speedup employing 8 remote accelerators from a single node with respect to a 12-core CPU-only execution. GPGPU service interaction in compute nodes, remote acceleration in dedicated GPGPU servers, and data transfer performance of similar GPU virtualization frameworks are also evaluated. 2014 Elsevier B.V. All rights reserved.This work was supported by the Spanish Ministerio de Economia y Competitividad (MINECO) and by FEDER funds under Grant TIN2012-38341-004-01. It was also supported by MINECO, FEDER funds, under Grant TIN2011-23283, and by the Fundacion Caixa-Castello Bancaixa, Grant P11B2013-21. This work was also supported in part by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. Authors are grateful for the generous support provided by Mellanox Technologies to the rCUDA Project. The authors would also like to thank Adrian Castello, member of The rCUDA Development Team, for his hard work on rCUDA.Peña Monferrer, AJ.; Reaño González, C.; Silla Jiménez, F.; Mayo Gual, R.; Quintana-Orti, ES.; Duato Marín, JF. (2014). A complete and efficient CUDA-sharing solution for HPC clusters. Parallel Computing. 40(10):574-588. https://doi.org/10.1016/j.parco.2014.09.011S574588401

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

A large number of sensitive homology searches are required for mapping DNA sequence fragments to known protein sequences in public and private databases during metagenomic analysis. BLAST is currently used for this purpose, but its calculation speed is insufficient, especially for analyzing the large quantities of sequence data obtained from a next-generation sequencer. However, faster search tools, such as BLAT, do not have sufficient search sensitivity for metagenomic analysis. Thus, a sensitive and efficient homology search tool is in high demand for this type of analysis.We developed a new, highly efficient homology search algorithm suitable for graphics processing unit (GPU) calculations that was implemented as a GPU system that we called GHOSTM. The system first searches for candidate alignment positions for a sequence from the database using pre-calculated indexes and then calculates local alignments around the candidate positions before calculating alignment scores. We implemented both of these processes on GPUs. The system achieved calculation speeds that were 130 and 407 times faster than BLAST with 1 GPU and 4 GPUs, respectively. The system also showed higher search sensitivity and had a calculation speed that was 4 and 15 times faster than BLAT with 1 GPU and 4 GPUs.We developed a GPU-optimized algorithm to perform sensitive sequence homology searches and implemented the system as GHOSTM. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We developed GHOSTM, which is a cost-efficient tool, and offer this tool as a potential solution to this problem

Lessons learnt on the analysis of large sequence data in animal genomics

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets

LNS Subtraction Using Novel Cotransformation and/or Interpolation

The Logarithmic Number System (LNS) makes multiplication, division and powering easy, but subtraction is expensive. Cotransformation converts the difficult operation of logarithmic subtraction into the easier operation of logarithmic addition. In this paper, a new variant of cotransformation is proposed, which is simpler to design and more economical in hardware than previous cotransformation methods. The novel method commutes operands differently for addition than for subtraction. Simulation results show how many guard bits are required by the new cotransformation to guarantee faithful rounding and that, even without guard bits, cotransformation produces an LNS unit more accurate than a previously published Hardware-Description-Language (HDL) library for LNS arithmetic that uses only multipartite tables or 2 nd-order interpolation

INVESTIGATION OF EQUILIBRIUM AND KINETIC ADSORPTION OF FREUNDLICH MODELS DURING PHOSPHORUS TRANSPORT IN THE SOIL

Στην εργασία αυτή αναπτύχθηκε ένα μαθηματικό μοντέλο μεταφοράς προσροφούμενων ουσιών στο έδαφος, υπό σταθερές συνθήκες μονοδιάστατης ροής του νερού, με τη μέθοδο των πεπερασμένων διαφορών του σχήματος Crank-Nicolson. Στο μοντέλο συμπεριλήφθηκαν οι εξισώσεις ισόθερμης προσρόφησης σε κατάσταση ισορροπίας και οι κινητικές εξισώσεις προσρόφησης τον Freundlich καθώς και οι εξισώσεις φθοράς της ουσίας από την υγρή και τη στερεά φάση του εδάφους. Η ακρίβεια του μοντέλου ελέγχθηκε με αναλυτικές λύσεις, με τα αποτελέσματα άλλων αριθμητικών λύσεων και με το ισοζύγιο μάζας. Από τη διερεύνηση των διαφορετικών μοντέλων προσρόφησης του Freundlich σε προβλήματα μεταφοράς μάζας του φωσφόρου, προέκυψε ότι, οι ισόθερμες εξισώσεις ισσοροπίας έδωσαν πολύ καλή προσέγγιση στην φάση της έκπλυσης της ουσίας, ενώ παρουσιάζουν σχετική αδυναμία στην περιγραφή της φάσης προσρόφησης της ουσίας. Οι κινητικές εξισώσεις προσεγγίζουν ικανοποιητικά τα πειραματικά δεδομένα. Εχουν τη δυνατότητα να δίνουν πολύ καλή προσέγγιση κατά την φάση προσρόφησης της ουσίας και υστερούν κάπως στην περιγραφή της φάσης έκπλυσης. Το μοντέλο προσρόφησης δύο κατευθύνσεων βελτιώνει την προσέγγιση των πειραματικών δεδομένων σε σχέση με αυτό της απλής κινητικής εξίσωσης.A mathematical model which describes the mass transport of adsorbed solutes in the soil, under one-dimensional steady state water flow conditions is developed. The model is based on finite difference method and the Crank-Nicolson approximation. The model incorporates the equilibrium and the kinetic equations of Freundlich adsorption, as well as the sink terms from the solution and solid phases. Comparison with analytical and other numerical solutions and mass balance criterion are used to test the accuracy of model. The investigation of different Freundlich adsorption models in phosphorus mass transport problems showed that the isotherm equilibrium equations provide an accurate aproximation during the desorption process, while they showed not good description during the sortion phase. The kinetic equations approach the experimental data very successful. These provide accurate results during the adsorption phase but less accuracy during the desorption. The model of two-site improved the accuracy of experimental data approximation