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May 03, 2012
A review of eXludus’ new micro-virtualization technology for multicore environments
In a previous article (We Need More than Multicore), I discussed the evolution of multicore processors, and the dramatic effect this processor shift can have on compute cluster performance. Clearly, leveraging a lot of cores will require that many concurrent tasks – as opposed to a single massively parallel task – run safely and predictably within a system. These concurrent tasks will range from serial to multi-threaded to parallel tasks, and all will need to share the same system resources in a productive and reliable manner.
The question becomes how to do this in operating systems (OS) environments that were not designed with multicore architectures in mind. For example, Linux, which has become the pervasive operating OS for servers, is based on time slicing, which is somewhat analogous to suboptimal round-robin server farm dispatch. But it has limitations when running many concurrent tasks that access shared processors and memory. As the number of competing tasks increases, the likelihood of interference between tasks rises exponentially.
The operating system has limited tools that identify escalating resource access problems or proactive defenses to resolve such problems. With these inherent limitations, users often realize reduced system performance and/or reliability. Nor can the OS manage work prioritization between tasks very well, so the established workload management priorities are largely lost when a mix of jobs from different queues are dispatched to a compute node. In summary, the major issues with today’s multicore systems include:
Multicore optimization specialist eXludus Technologies believes it has the answer. The company recently announced the industry’s first micro-virtualization solution. This software creates lightweight micro-containers that encapsulate one or more applications, and are based upon defined application or project policies. These containers have embedded resource allocation intelligence that applies predictive queuing algorithms in order to optimize allocation of micro-resources, such as cores and memory. And it does so in real time.
With negligible system and administrative overhead, the eXludus solution expands the use case for virtualization, making it suitable for performance-sensitive environments (most notably, HPC) that have previously avoided virtualization because of overhead concerns.
The software promises to extract up to 70 percent more throughput from the same resources, while acting as a safety net to avoid resource over-subscription that is detrimental to system performance. Since the micro-containers run within an OS, the eXludus software can be deployed separately from or together with existing server and storage virtualization solutions.
By applying virtualization underneath the OS, a number of benefits are exposed. For example, although it’s easy to load a system, the challenge is achieving maximum utilization while avoiding resource oversubscription, which leads to performance reduction and system instability. Using a lightweight framework, micro-virtualization automates the process of optimizing resources, safely allowing system utilization to be increased. In more detail, micro-virtualization is designed to:
The support of SLAs is particularly important. Multi-tenancy invariably grows with core counts, either via cloud-like unrelated users or within the enterprise, where various departments or projects end up sharing systems to a greater degree. These multi-tenants pay in some manner, either directly or via budget contribution, so they demand to get what they pay for. Therefore, consistent and predictable results are important, i.e., a user can't complete processing in X time on one iteration and 2X on the next iteration.
Micro-virtualization provides mechanisms that ensure that specific applications, users, and projects receive the CPU and memory resources that have been paid for or committed to. Specifically, administrators can declare CPU and memory percentages that guarantee resource levels.
Within the kernel, multi-tenant/multi-user/multi-project work falls subject to equal OS time-slice behavior. That’s true even though the work, which ranges from high-cost applications to open-source, is not all equal. Users can easily run into situations where high-value work is slowed by low-value work. Micro-virtualization provides tools that allow for discrete task prioritization that can predictably steer resource allocations.
Within an enterprise there may be many iterations of an application running, but each iteration may have unique value. Consider a chip-design environment where multiple next-gen processors are under development. The soon-to-be-released processor has more enterprise value than a design to be completed in three years. Micro-virtualization easily accommodates resource steering to the high value work, even to the extent that work can be flagged as having exclusive access to system resources -- think of a chip tape-out that needs last minute fixes, or rendering where a movie is about to be released and maximum performance is required.
The eXludus solution also promises to open up HPC to virtualization. Legacy server virtualization has not been very successful in these situations as the heavyweight hypervisor approach has high overhead costs. Micro-virtualization is truly lightweight, in the range of 1 to 2 percent overhead, whereas full server virtualization may entail an 18 to 20 penalty in these scenarios. And where full virtualization complexity is administratively cumbersome, micro-virtualization is simple and literally can be deployed within hours.
A current limitation of eXludus micro-virtualization is that all work must be run within a single OS environment, in this case Linux. And the solution does not yield an easily predictable or consistent performance gain. Throughput increases are a function of the workload, which users may find difficult to comprehend.
In aggregate though, micro-virtualization as a means to effectively segment multicore systems and extract maximum efficiencies appears to be an idea whose time has come. Service efficiency is improved and resources are more accurately steered to the highest value tasks in support of business objectives.
Wolfgang Gentzsch is an independent HPC consultant for cluster, grid, and cloud computing, technologies and a member of the Board of Directors for eXludus Technologies.
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When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
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