Invasive Computing in HPC with X10

High performance computing with thousands of cores relies on distributed memory due to memory consistency reasons. The resource management on such systems usually relies on static assignment of resources at the start of each application. Such a static scheduling is incapable of starting applications with required resources being used by others since a reduction of resources assigned to applications without stopping them is not possible. This lack of dynamic adaptive scheduling leads to idling resources until the remaining amount of requested resources gets available. Additionally, applications with changing resource requirements lead to idling or less efficiently used resources. The invasive computing paradigm suggests dynamic resource scheduling and applications able to dynamically adapt to changing resource requirements. As a case study, we developed an invasive resource manager as well as a multigrid with dynamically changing resource demands. Such a multigrid has changing scalability behavior during its execution and requires data migration upon reallocation due to distributed memory systems. To counteract the additional complexity introduced by the additional interfaces, e. g. for data migration, we use the X10 programming language for improved programmability. Our results show improved application throughput and the dynamic adaptivity. In addition, we show our extension for the distributed arrays of X10 to support data migrationThis work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre “Invasive Computing” (SFB/TR 89)

A UPC++ Actor Library and Its Evaluation on a Shallow Water Proxy Application

Programmability is one of the key challenges of Exascale Computing. Using the actor model for distributed computations may be one solution. The actor model separates computation from communication while still enabling their over-lap. Each actor possesses specified communication endpoints to publish and receive information. Computations are undertaken based on the data available on these channels. We present a library that implements this programming model using UPC++, a PGAS library, and evaluate three different parallelization strategies, one based on rank-sequential execution, one based on multiple threads in a rank, and one based on OpenMP tasks. In an evaluation of our library using shallow water proxy applications, our solution compares favorably against an earlier implementation based on X10, and a BSP-based approach

Resource-aware Programming in a High-level Language - Improved performance with manageable effort on clustered MPSoCs

Bis 2001 bedeutete Moores und Dennards Gesetz eine Verdoppelung der Ausführungszeit alle 18 Monate durch verbesserte CPUs. Heute ist Nebenläufigkeit das dominante Mittel zur Beschleunigung von Supercomputern bis zu mobilen Geräten. Allerdings behindern neuere Phänomene wie "Dark Silicon" zunehmend eine weitere Beschleunigung durch Hardware. Um weitere Beschleunigung zu erreichen muss sich auch die Soft­ware mehr ihrer Hardware Resourcen gewahr werden. Verbunden mit diesem Phänomen ist eine immer heterogenere Hardware. Supercomputer integrieren Beschleuniger wie GPUs. Mobile SoCs (bspw. Smartphones) integrieren immer mehr Fähigkeiten. Spezialhardware auszunutzen ist eine bekannte Methode, um den Energieverbrauch zu senken, was ein weiterer wichtiger Aspekt ist, welcher mit der reinen Geschwindigkeit abgewogen werde muss. Zum Beispiel werden Supercomputer auch nach "Performance pro Watt" bewertet. Zur Zeit sind systemnahe low-level Programmierer es gewohnt über Hardware nachzudenken, während der gemeine high-level Programmierer es vorzieht von der Plattform möglichst zu abstrahieren (bspw. Cloud). "High-level" bedeutet nicht, dass Hardware irrelevant ist, sondern dass sie abstrahiert werden kann. Falls Sie eine Java-Anwendung für Android entwickeln, kann der Akku ein wichtiger Aspekt sein. Irgendwann müssen aber auch Hochsprachen resourcengewahr werden, um Geschwindigkeit oder Energieverbrauch zu verbessern. Innerhalb des Transregio "Invasive Computing" habe ich an diesen Problemen gearbeitet. In meiner Dissertation stelle ich ein Framework vor, mit dem man Hochsprachenanwendungen resourcengewahr machen kann, um so die Leistung zu verbessern. Das könnte beispielsweise erhöhte Effizienz oder schnellerer Ausführung für das System als Ganzes bringen. Ein Kerngedanke dabei ist, dass Anwendungen sich nicht selbst optimieren. Stattdessen geben sie alle Informationen an das Betriebssystem. Das Betriebssystem hat eine globale Sicht und trifft Entscheidungen über die Resourcen. Diesen Prozess nennen wir "Invasion". Die Aufgabe der Anwendung ist es, sich an diese Entscheidungen anzupassen, aber nicht selbst welche zu fällen. Die Herausforderung besteht darin eine Sprache zu definieren, mit der Anwendungen Resourcenbedingungen und Leistungsinformationen kommunizieren. So eine Sprache muss ausdrucksstark genug für komplexe Informationen, erweiterbar für neue Resourcentypen, und angenehm für den Programmierer sein. Die zentralen Beiträge dieser Dissertation sind: Ein theoretisches Modell der Resourcen-Verwaltung, um die Essenz des resourcengewahren Frameworks zu beschreiben, die Korrektheit der Entscheidungen des Betriebssystems bezüglich der Bedingungen einer Anwendung zu begründen und zum Beweis meiner Thesen von Effizienz und Beschleunigung in der Theorie. Ein Framework und eine Übersetzungspfad resourcengewahrer Programmierung für die Hochsprache X10. Zur Bewertung des Ansatzes haben wir Anwendungen aus dem High Performance Computing implementiert. Eine Beschleunigung von 5x konnte gemessen werden. Ein Speicherkonsistenzmodell für die X10 Programmiersprache, da dies ein notwendiger Schritt zu einer formalen Semantik ist, die das theoretische Modell und die konkrete Implementierung verknüpft. Zusammengefasst zeige ich, dass resourcengewahre Programmierung in Hoch\-sprachen auf zukünftigen Architekturen mit vielen Kernen mit vertretbarem Aufwand machbar ist und die Leistung verbessert

Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights

There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve functional tumour-associated blood vessel deterioration and normalisation. The former aims at tumour infarction and nutrient deprivation medi- ated by vascular targeting agents that induce occlusion/collapse of tumour blood vessels. In contrast, the therapeutic intention of normalising the abnormal structure and function of tumour vascular net- works, e.g. via alleviating stress-induced vaso-occlusion, is to improve chemo-, immuno- and radiation therapy efficacy. Although both strategies have shown therapeutic potential, it remains unclear why they often fail to control glioma invasion into the surrounding healthy brain tissue. To shed light on this issue, we propose a mathematical model of glioma invasion focusing on the interplay between the mi- gration/proliferation dichotomy (Go-or-Grow) of glioma cells and modulations of the functional tumour vasculature. Vaso-modulatory interventions are modelled by varying the degree of vaso-occlusion. We discovered the existence of a critical cell proliferation/diffusion ratio that separates glioma invasion re- sponses to vaso-modulatory interventions into two distinct regimes. While for tumours, belonging to one regime, vascular modulations reduce the tumour front speed and increase the infiltration width, for those in the other regime the invasion speed increases and infiltration width decreases. We show how these in silico findings can be used to guide individualised approaches of vaso-modulatory treatment strategies and thereby improve success rates

HIGH PERFORMANCE MODELLING AND COMPUTING IN COMPLEX MEDICAL CONDITIONS: REALISTIC CEREBELLUM SIMULATION AND REAL-TIME BRAIN CANCER DETECTION

The personalized medicine is the medicine of the future. This innovation is supported by the ongoing technological development that will be crucial in this field. Several areas in the healthcare research require performant technological systems, which elaborate huge amount of data in real-time. By exploiting the High Performance Computing technologies, scientists want to reach the goal of developing accurate diagnosis and personalized therapies. To reach these goals three main activities have to be investigated: managing a great amount of data acquisition and analysis, designing computational models to simulate the patient clinical status, and developing medical support systems to provide fast decisions during diagnosis or therapies. These three aspects are strongly supported by technological systems that could appear disconnected. However, in this new medicine, they will be in some way connected. As far as the data are concerned, today people are immersed in technology, producing a huge amount of heterogeneous data. Part of these is characterized by a great medical potential that could facilitate the delineation of the patient health condition and could be integrated in our medical record facilitating clinical decisions. To ensure this process technological systems able to organize, analyse and share these information are needed. Furthermore, they should guarantee a fast data usability. In this contest HPC and, in particular, the multicore and manycore processors, will surely have a high importance since they are capable to spread the computational workload on different cores to reduce the elaboration times. These solutions are crucial also in the computational modelling, field where several research groups aim to implement models able to realistically reproduce the human organs behaviour to develop their simulators. They are called digital twins and allow to reproduce the organ activity of a specific patient to study the disease progression or a new therapy. Patient data will be the inputs of these models which will predict her/his condition, avoiding invasive and expensive exams. The technological support that a realistic organ simulator requires is significant from the computational point of view. For this reason, devices as GPUs, FPGAs, multicore devices or even supercomputers are needed. As an example in this field, the development of a cerebellar simulator exploiting HPC will be described in the second chapter of this work. The complexity of the realistic mathematical models used will justify such a technological choice to reach reduced elaboration times. This work is within the Human Brain Project that aims to run a complete realistic simulation of the human brain. Finally, these technologies have a crucial role in the medical support system development. Most of the times during surgeries, it is very important that a support system provides a real-time answer. Moreover, the fact that this answer is the result of the elaboration of a complex mathematical problem, makes HPC system essential also in this field. If environments such as surgeries are considered, it is more plausible that the computation is performed by local desktop systems able to elaborate the data directly acquired during the surgery. The third chapter of this thesis describes the development of a brain cancer detection system, exploiting GPUs. This support system, developed as part of the HELICoiD project, performs a real-time elaboration of the brain hyperspectral images, acquired during surgery, to provide a classification map which highlights the tumor. The neurosurgeon is facilitated in the tissue resection. In this field, the GPU has been crucial to provide a real-time elaboration. Finally, it is possible to assert that in most of the fields of the personalized medicine, HPC will have a crucial role since they consist in the elaboration of a great amount of data in reduced times, aiming to provide specific diagnosis and therapies for the patient

A Scalable and Adaptive Network on Chip for Many-Core Architectures

In this work, a scalable network on chip (NoC) for future many-core architectures is proposed and investigated. It supports different QoS mechanisms to ensure predictable communication. Self-optimization is introduced to adapt the energy footprint and the performance of the network to the communication requirements. A fault tolerance concept allows to deal with permanent errors. Moreover, a template-based automated evaluation and design methodology and a synthesis flow for NoCs is introduced

Sequencing impact at the University of Missouri

Executive Summary: It would be an understatement to say that "next-generation" sequencing technology has been revolutionary. Over the last 10 years, sequencing has created a paradigm shift in biological sciences where more and more a component of research involves "just sequence it". This is because the types of data, applications and resulting insights are expanding every year. Further, the volume and speed of data generation are growing exponentially, while the costs to generate these data are decreasing exponentially. The Human Genome Project completed the first draft genome sequence in 2001 at an estimated cost of $3 billion. Next-generation sequencing became mainstream around 2007 and enabled the re-sequencing of a human genome at a cost of approximately$50,000. In late 2015, Illumina announced the availability of their X10 sequencer for use on non-human samples enabling the re-sequencing of a mammalian (human, cow, dog etc.) genome for approximately $1,500 and with an annual throughput of 10,000 genomes per year. The ease, rapidity and cost effectiveness of generating sequence data has created a computational analysis bottleneck. The growth of computational resources on the MU campus has not kept pace with the growth in data generation capability. In order for Mizzou to maintain a competitive research environment, we need to expand the computational resources available for bioinformatics analysis of large data which include sequence data. It will require an initial investment of$619,000 in early 2016 to build the needed core infrastructure and will require ongoing funding to maintain and expand this infrastructure. Initial investments (cost share of $231,000) made by Mizzou in 2005 to bring next-generation sequencing to this campus have been returned many-fold. Based on a survey sent to MU researchers in November 2015, a total of 66 grants have been awarded involving sequencing for a total of$87.5M. $7.6M of that is directly attributable to sequence data generation/analysis. In addition, another$7.9M in grant funding has been submitted and remains pending. This research has led to 173 refereed journal articles in top-tier journals producing over 6,000 citations. Additionally, 19 M.S., 62 Ph.D. and 21 postdocs have been trained as a result of these sequence related research projects. Plant and animal researchers at MU have been at the forefront of the next-generation sequencing revolution. However, based on the diversity of grants and papers gathered by the survey, sequence analysis provides a common foundation that ties together many disciplines on campus. As such, investment in computational capacity directed at sequence data analysis will serve the entire campus and provide technological ties between disciplines. The following is a detailed description of the history of sequencing/bioinformatics, a description of the computation resources required, and a model for sustainability and an analysis of the impacts of next-generation sequencing at Mizzou

Functional and structural properties of spatial processing networks in the brain

The Perceptual-Mnemonic (PM) view of the Medial Temporal Lobe (MTL) suggests it processes representations for both perception and memory and that functional separation in its regions echoes differing modality specialisation of two widespread networks. This thesis investigated a Posteromedial Network (PMN) facilitating spatiotemporal navigation, contrasting it with an Anteroinferior Network (AIN) facilitating aggregate object/face processing. 3 Supporting the PM-view, previous work reported correlations between network tract microstructure, functional magnetic resonance imaging-measured MTL signals and perceptual performance. However: the microstructure measures were biologically non-specific; no studies used magnetoencephalography (more temporally precise); the relative importance in MTL-reliant behaviours of PMN tracts that connect different MTL areas were uninvestigated; and studies relating PMN network structure to temporal processing produced conflicting results. 4 This project investigated relationships between inter-individual differences in behaviour and these networks’ structures and functions using perceptual and mnemonic tasks probing perception of scenes and faces, and memory of objects-in�sequences. Microstructure measures were reduced into biologically interpretable components. Those of the fornix – a proxy of hippocampal-PMN communication – correlated with scene perception and object-in-sequence memory performance. Those of the parahippocampal cingulum, which connects other PMN areas, did not, indicating the specific role of the hippocampus in spatiotemporal representation. Those of the inferior longitudinal fasciculus, part of the AIN, correlated with face perception performance. PMN theta/gamma power modulation occurred more during scene perception than face perception. In-task MTL theta power modulation (reflecting hippocampal/parahippocampal processing), and PMN - posterior cingulate cortex resting-state connectivity correlated with scene perception performance. Conversely, AIN theta/gamma power modulations occurred during face perception. These imply that MTL regions are important for both perception and memory and that two dissociable networks cater for the different modalities. An implication of the findings is that MTL damage (e.g., as occurs in Alzheimer’s Disease) may not produce purely memory disorders but impair representations for use across behaviours