Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications

Sisäkkäiset virtuaaliympäristöt

Virtual Machines have been a common computation platform in areas of cloud computing for some time now. VMs offer a decent amount of isolation for security and system resources, and from application perspective they behave much like native environments. Software containers are gaining popularity, as a new application delivery technology. Just like VMs, applications started inside containers are running in isolated environments but without the performance overhead caused by virtualization of system resources. This makes containers seem like a more effient option for VMs. In this thesis, different combinations of containers and VMs are benchmarked. For each benchmark, host environment is also measured, to understand the overhead caused by the underlying virtuel environment technology. Benchmarks used include storage and network access benchmarks, and also an application benchmark of compiling Linux kernel. As another part of the thesis, a CPU intensive workload is run on the virtualization host server. Then the benchmarks are repeated, in order to determine how much the given workload effects the benchmark score, and also if this effect can be observed from the virtualization guest side by measuring CPU steal time. Results show that containers are slightly slower in the application benchmark than the host. The main difference is expected to come from the way docker handles storage accesses. With default network configuration, the container is losing in terms of performance to the host. In every benchmark we did, VMs always lost to host and containers in performance.Virtuaalikoneista on tullut yleinen laskenta-alusta pilvitietokoneille. Ne eristävät virtuaaliympäristön muista palveluista samalla fyysisellä koneella ja sovellusten näkökulmasta ne toimivat lähes samalla tavalla kuin natiivit ympäristöt. Ohjelmistokontit ovat nousseet suosioon tehokkaana sovellusten toimitusteknologiana. Molemmat, sekä virtuaalikoneet, että ohjelmistokontit tarjoavat niiden sisällä suoritettaville sovelluksille eristetyn virtuaaliympäristön. Ohjelmistokontit eivät pyri virtualisoimaan kaikkia järjestelmän resursseja vaan käyttävät alla olevaa käyttöjärjestelmän ydintä hyväkseen. Tämä tekee ohjelmistokonteista houkuttelevan vaihtoehdon virtuaalikoneille. Tässä diplomityössä suoritettiin erilaisia suorituskykymittauksia ohjelmistokonttien ja virtuaalikoneiden avulla luoduissa ympäristöissä. Myös alla olevan isäntäkoneen natiivisuorituskyky mitattiin, josta saatiin hyvä arvo erilaisten virtuaaliympäristöjen vertailuun. Mittasimme pysyvän muistin, verkon ja sovelluksen suorituskyvyn. Sovelluksena toimi Linuxin kääntäminen lähdekoodista toimivaksi käyttöjärjestelmäksi. Tuloksemme osoittavat, että sovellussuorituskykytestissä kontit häviävät natiivijärjestelmän suorituskyvylle vain vähän. Eron oletetaan johtuvan tavasta, jolla valitsemamme konttiteknologia hoitaa pysyvän muistin lukemisen ja kirjoittamisen. Oletusverkkoasetuksilla, kontit hävisivät natiivijärjestelmälle myös. Kaikissa tekemissämme suorituskykymittauksissa virtuaalikoneet hävisivät natiivijärjestelmälle sekä ohjelmistokonteille

Glider: A GPU Library Driver for Improved System Security

Legacy device drivers implement both device resource management and isolation. This results in a large code base with a wide high-level interface making the driver vulnerable to security attacks. This is particularly problematic for increasingly popular accelerators like GPUs that have large, complex drivers. We solve this problem with library drivers, a new driver architecture. A library driver implements resource management as an untrusted library in the application process address space, and implements isolation as a kernel module that is smaller and has a narrower lower-level interface (i.e., closer to hardware) than a legacy driver. We articulate a set of device and platform hardware properties that are required to retrofit a legacy driver into a library driver. To demonstrate the feasibility and superiority of library drivers, we present Glider, a library driver implementation for two GPUs of popular brands, Radeon and Intel. Glider reduces the TCB size and attack surface by about 35% and 84% respectively for a Radeon HD 6450 GPU and by about 38% and 90% respectively for an Intel Ivy Bridge GPU. Moreover, it incurs no performance cost. Indeed, Glider outperforms a legacy driver for applications requiring intensive interactions with the device driver, such as applications using the OpenGL immediate mode API

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

An innovative approach to performance metrics calculus in cloud computing environments: a guest-to-host oriented perspective

In virtualized systems, the task of profiling and resource monitoring is not straight-forward. Many datacenters perform CPU overcommittment using hypervisors, running multiple virtual machines on a single computer where the total number of virtual CPUs exceeds the total number of physical CPUs available. From a customer point of view, it could be indeed interesting to know if the purchased service levels are effectively respected by the cloud provider. The innovative approach to performance profiling described in this work is based on the use of virtual performance counters, only recently made available by some hypervisors to their virtual machines, to implement guest-wide profiling. Although it isn't possible for the virtual machine to access Virtual Machine Monitor, with this method it is able to gather interesting informations to deduce the state of resource overcommittment of the virtualization host where it is executed. Tests have been carried out inside the compute nodes of FIWARE Genoa Node, an instance of a widely distributed federated community cloud, based on OpenStack and KVM. AgiLab-DITEN, the laboratory I belonged to and where I conducted my studies, together with TnT-Lab\u2013DITEN and CNIT-GE-Unit designed, installed and configured the whole Genoa Node, that was hosted on DITEN-UniGE equipment rooms. All the software measuring instruments, operating systems and programs used in this research are publicly available and free, and can be easily installed in a micro instance of virtual machine, rapidly deployable also in public clouds