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Results 1 - 16 of 23 Market–Oriented Grid and Utility Computing (Wiley Series on Parallel and Distributed Computing). 20 November by Rajkumar downloadya. High Performance Cluster. Computing: Architectures and Systems, Volume 1. Edited by. Rajkumar downloadya [email protected] School of Computer . 1 -- High Performance Cluster Computing: Architectures and Systems, Rajkumar Cluster Computing: Programming and Applications, Rajkumar downloadya (editor).
Volume 1 of this two-volume set collected today's best work on the systems aspects of high performance cluster computing. Now, in High Performance Cluster Computing: Programming and Application Issues, Volume 2, Rajkumar downloadya brings together the world's leading work on programming and applications for today's state-of-the-art "commodity supercomputers.
All three areas have seen major advances in recent years-and in all three areas, this book offers unprecented breadth and depth.
Coverage includes: Code liberation, global renaming, name reclamation, and debugging interfaces. Designing operating system services for wide-area applications.
Distributed objects, the HPspmd model, and more. Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support? Volume 2 discusses programming environments and development tools, Java as a language of choice for development in highly parallel systems, and state-of-the-art high performance algorithms and applications. High performance computing. Read more Read less.
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From the Inside Flap Preface The initial idea leading to clusters computing was developed in the s by IBM as a way of linking large mainframes to provide a cost-effective form of commercial parallelism. Read more.
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site Inspire Digital Educational Resources. site Rapids Fun stories for kids on the go. site Restaurants Food delivery from local restaurants. A comparison of the two approaches in order to point out their advantages and drawbacks, as far as performance is concerned. New performance evaluation techniques and tools to support HPC in cloud systems.
As outlined in the previous sections, the adoption of the cloud paradigm for HPC is a flexible way to deploy virtual clusters dedicated to execute HPC applications.
In the latter, every time that a task is started, the user will be charged for the used resources. But it is very hard to know in advance which will be the resource usage and hence the cost.
On the other hand, even if the global expense for a physical cluster is higher, once the system has been acquired, all the costs are fixed and predictable in fact, they are so until the system is not faulty.
It would be great to predict, albeit approximately, the resource usage of a target application in a cloud, in order to estimate the cost of its execution. These two issues above are strictly related, and a performance problem becomes an economic problem. Let us assume that a given application is well- optimized for a physical cluster.
If it behaves on a virtual cluster as on the physical one, it will use the cloud resources in an efficient way, and its execution will be relatively cheap. This is not so trivial as it may seem, as the pay-per-use paradigm commonly used in commercial clouds see Table Almost surprisingly, this means that processor idle time has a cost for cloud users. Let us consider two different virtual clusters with two and four nodes, respectively.
Let us assume that the application is well-optimized and that, at least for a small number of processors, it gets linear speed-up. The target application will be executed in two hours in the first cluster and in one hour in the second one. Let the execution cost be X dollars per hour per machine instance virtual node. This is similar to the charging scheme of EC2.
It turns out that the two configurations have the same cost for the final user, even if the first execution is slower than the second. Now if we consider an application that is not well-optimized and has a speed-up less than the ideal one, the running time on the large virtual cluster will be longer than two hours; as a consequence, the cost of the run of the second virtual cluster TABLE In conclusion: In clouds, performance counts two times.
Low performance means not only long waiting times, but also high costs. The use of alternative cost factors e. The advanced user, on the other hand, would also know if there is a way to optimize its application so as to reduce the cost of its run without sacrificing performance. The high-end user, who cares more for performance than for the cost to be sustained, would like instead to know how to choose the best configuration to maximize the performance of his application.
In other words, in the cloud world the hardware configuration is not fixed, and it is not the starting point for optimization decisions. Config- urations can be easily changed in order to fit the user needs.
All the three classes of users should resort to performance analysis and prediction tools. But, unfortunately, prediction tools for virtual environments are not available, and the literature presents only partial results on the performance analysis of such systems.
An additional consequence of the different way that HPC users exploit a virtual cluster is that the cloud concept makes very different the system dimensioning—that is, the choice of the system configuration fit for the user purposes cost, maximum response time, etc.
An HPC machine is chosen and acquired, aiming to be at the top of available technology under inevitable money constraints and to be able to sustain the highest system usage that may eventually be required. In other words, the dimensioning is made by considering the peak system usage.
It takes place at system acquisition time, by examining the machine specifications or by assembling it using hardware components of known performance. In this phase, simple and global performance indexes are used e. In clouds, instead, the system must be dimensioned by finding out an optimal trade-off between application performance and used resources.
As mentioned above, the optimality is a concept that is fairly different, depending on the class of users. Someone would like to obtain high performance at any cost, whereas others would privilege economic factors. In any case, as the choice of the system is not done once and for all, the dimensioning of the virtual clusters takes place every time the HPC applications have to be executed on new datasets.
In clouds, the system dimensioning is a task under the control of the user, not of the system administrator. Table To summarize the above discussion, in systems the clouds where the availability of performance data is crucial to know how fast your applications will run and how much you will pay, there is great uncertainty about what to measure and how to measure, and there are great difficulties when attempting to interpret the meaning of measured data.
This will make it possible to point out the advantages and disadvantages of the two approaches and will enable us to understand if and when clouds can be useful for HPC. The performance characterization of HPC systems is usually carried out by executing benchmarks. As performance index, together with FLOPS, it measures response time, network bandwidth usage, and latency.
It measures average response time, network bandwidth usage and latency. In the HPC world, these benchmarks are of common use and widely diffused, but their utility is limited. Users usually have an in-depth knowledge of the target hardware used for executing their applications, and a comparison between two different physical clusters makes sense only for Top classification or when they are acquired.
HPC users usually outline the potentiality and the main features of their system through a a brief description of the hardware and b a few performance indexes obtained using some of the above-presented benchmarks. In any case, these descriptions are considered useless for application performance optimization, because they only aim at providing a rough classification of the hardware.
Recently, the benchmarking technique has been adopted in a similar way, tackling also the problem of the utility of the cloud paradigm for scientific applications.
In particular, the papers focusing on the development of applica- tions executed in virtual clusters propose the use of a few benchmarks to outline the hardware potentialities [22, 23]. These results are of little interest for our comparison. On the other hand, papers that present comparisons between virtual and physical clusters [18, 20 22, 36, 37] use benchmarks to find out the limits of cloud environments, as discussed below.
In the following, we will focus on these results. We can start our analysis from benchmark-based comparison of virtual clusters and physical HPC systems. In the literature there are results on all three types of benchmarks mentioned above, even if the only cloud provider considered is site EC2 there are also results on private clusters, but in those cases the analysis focuses on virtual engine level and neglects the effects of the cloud environment, and so it is outside the scope of this chapter.
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Napper and Bientinesi  and Ostermann et al. Both studies point out that the values obtained in the VCs are an order of magnitude lower than equivalent solutions on physical clusters.
Even if it is reasonable that VCs peak performances are far from the supercomputer ones, it is worth noting that the GFLOPS tends to decrease being fixed the memory load when the number of nodes increases.
In other words, virtual clusters are not so efficient as physical clusters, at least for this benchmark. As shown later, the main cause of this behavior is the inadequate internal interconnect. Walker  compared a virtual EC2 cluster to a physical cluster composed of TeraGrid machines with similar hardware configuration i.
This comparison pointed out that the overheads introduced by the virtualization layer and the cloud environment level were fairly high. It should be noted that Walker adopted for his analysis two virtual clusters made up of a very limited number of nodes two and four. But, even for such small systems, the applications did not scale well with the number of nodes. The last kind of benchmark widely adopted in the literature is the MPI kernel benchmark, which measures response time, bandwidth, and latency for MPI communication primitives.
These tests, proposed by almost all the authors who tried to run scientific applications on cloud-based virtual clusters, are coherent with the results presented above. In all the cases in the literature, bandwidth and, above all, latency have unacceptable values for HPC applications.
As mentioned above, the benchmarking technique is able to put in evidence the main drawback linked to the adoption of cloud systems for HPC: the unsatisfactory performance of the network connection between virtual clusters.
In any case, the performance offered by virtual clusters is not comparable to the one offered by physical clusters. Even if the results briefly reported above are of great interest and can be of help to get insight on the problem, they do not take into account the differences between HPC machines and HPC in the cloud, which we have summarized at the start of this section. Stated another way, the mentioned analyses simply measure global performance indexes.
But the scenario can drastically change if different performance indexes are measured. Just to start, the application response time is perhaps the performance index of great importance in a cloud context. In fact, it is a measurement of interest for the final user and, above all, has a direct impact on the cost of the application execution.
An interesting consideration linked to response time was proposed by Ian Foster in his blog . The overall application response time RT is given by the formula RT 5 h job submission timei 1 hexecution timei. On the other hand, in a cloud used to run HPC workload a virtual cluster dedicated to the HPC user , queues and waiting time simply disappear. The result is that, even if the virtual cluster may offer a much lower computational power, the final response time may be comparable to that of physical HPC systems.
In order to take into account this important difference between physical and virtual environments, Foster suggests to evaluate the response time in terms of probability of completion, which is a stochastic function of time, and represents the probability that the job will be completed before that time. Note that the stochastic behavior mainly depends on the job submission time, whereas execution time is usually a deterministic value.
So in a VC the probability of In a typical HPC environment, which involves batch and queuing systems, the job submission time is stochastic and fairly long, thus leading to a global completion time higher than the one measured on the VC.
This phenomenon opens the way to a large adoption of the cloud approach, at least for middle- or small-dimension HPC applications, where the computa- tion power loss due to the use of the cloud is more tolerable. In Jha et al. This is a context in which the grid paradigm was never largely adopted because of the high startup overhead.
To support HPC applications, a fundamental requirement from a cloud provider is that an adequate service-level agreement SLA is granted. For HPC applications, the SLA should be different from the ones currently offered for the most common uses of cloud systems, oriented at transactional Web applications. The SLA should offer guarantees useful for the HPC user to predict his application performance behavior and hence to give formal or semi- formal statements about the parameters involved.
At the state of the art, cloud providers offer their SLAs in the form of a contract hence in natural language, with no formal specification. The first one site stresses fault tolerance parameters such as service uptime , offering guarantees about system availability.
Moreover, site guarantees that the virtual machine instances will run using a dedicated memory i.
This statement is particularly relevant for HPC users, because it is of great help for the performance predictability of applications. On the other hand, GoGrid, in addition to the availability parameters, offers a clear set of guarantees on network parameters, as shown in Table This kind of information is of great interest, even if the guaranteed network latency order of milliseconds is clearly unacceptable for HPC applications.
In conclusion, even if the adoption of SLA could be part of a solution for HPC performance tuning, giving a clear reference for the offered virtual cluster performances, current solutions offer too generic SLA contracts or too poor values for the controlled parameters.
As regards performance measurement techniques and tools, along with their adaption for virtualized environments, it should be noted that very few performance-oriented services are offered by cloud providers or by third parties. Usually these services simply consist of more or less detailed performance monitoring tools, such as CloudWatch offered by site, or CloudStatus, offered by Hyperic and integrated in site.
These tools essentially measure the performance of the cloud internal or external network and should help the cloud user to tune his applications. In exactly the same way as SLAs, they can be useful only for the transactional applications that are the primary objective of cloud systems, since, at the state of the art, they do not offer any features to predict the behavior of long-running applications, such as HPC codes.
An interesting approach, although still experimental, is the one offered by solutions as C-meter  and PerfCloud , which offer frameworks that dynamically benchmark the target VMs or VCs offered by the cloud.
The idea is to provide a benchmark-on-demand service to take into account the extreme variability of the cloud load and to evaluate frequently its actual state. The first framework  supports the GrenchMark benchmark which generates syn- thetic workloads and is oriented to Web applications. More detailed, the PerfCloud project aims at providing performance evaluation and prediction services in grid-based clouds.
Besides providing services for on- demand benchmarking of virtual clusters, the PerfCloud framework uses the benchmarking results to tune a simulator used for predict the performance of HPC applications. From the analysis of existing work, a number of considerations arise. Here we will try to summarize those that in our opinion are the most relevant ones. First of all, current cloud interconnects are simply not suitable for HPC uses. The performance of a gigabit or gigabit Ethernet is very good for running workloads made up of monolithic tasks, but it is inadequate for the majority of HPC parallel tasks.
Upgrading existing clouds so as to provide high- performance interconnects is not just an economic matter. Up until now, drivers for these interconnects are not supported by state-of-the-art virtual engines. And, as we have repeated many times in this chapter, virtual engines Download from Wow! Secondly, the SLAs that have proven to be extremely useful in different contexts have finally appeared in the commercial cloud field. This is a good starting point.
But the problem is that their current formulation is once again completely inadequate to express a quality of service that could be of interest for HPC users.
These need SLA defined in a more formal way, along with guarantees of particular parameters essentially, low communication latency, even if associated to higher jitter values. But maybe the most important of the issues discussed here is that the criteria for computing the cost of an application run do not encourage HPC users to resort to clouds. Commercial cloud providers try to give machines in exclusive use for computationally intensive tasks, and hence the cost to pay for this is proportional to the total duration of the run.
This is natural, after all. But this choice penalizes the user that submits unoptimized applications, who pays even for the application idle time. And this, from his point of view, is unfair. Because an application well-optimized for a physical HPC system could likely be a non- optimized application in the virtual world of clouds e.
Furthermore, the mentioned problem makes it particularly difficult to estimate the cost of the run of an application at least, of its first run. We would like to conclude by pointing out that the HPC community has a lot of work to do in order to make cloud more useful for their needs. The use of virtualization and of leased computing resources is unstoppable and is an unavoidable technologic trend, at least due to the power savings that it implies.
High-end HPC users would difficultly resort to clouds. Or, at least, they would not resort to present-day clouds. This requires the availability of more insight on the performance of virtual environments, the development of virtual-enabled drivers for high-speed interconnects, and a pervasive use of performance evaluation techniques. Performance prediction of virtual and cloud-based systems is indeed possible, and some of the authors of this chapter are already working on it , but a lot of research and development work is still necessary to have tools that could be used by the typical user without hassle.
Sugerman, G. Venkitachalam, and B. Sotomayor, R. Montero, I. Llorente, and I. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L.
Youseff, and D. Keahey, I. Foster, T. Freeman, and X.
Zhang, Virtual workspaces: Achieving quality of service and quality of life in the grid, Scientific Programming, 4 13 , Papadopoulos, M. Katz, and G.Seitz, J. Basically, this engine provides by means of a hypervisor the illusion of multiple independent replicas of every physical machine in the cloud. At least in theory, the number of VMs is limited only by resource consumption typically, physical memory. In all the cases in the literature, bandwidth and, above all, latency have unacceptable values for HPC applications.
Scheduling with deadlines and loss functions. All the three classes of users should resort to performance analysis and prediction tools. Barroso, J. Berten, P. The University of Manitoba Canada 3. Ghattas, E.
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