Dynamic Multigrain Parallelization on the Cell Broadband Engine

This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present policies and mechanisms for adaptive exploitation and scheduling of multiple layers of parallelism on the Cell Broadband Engine. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exposed from the runtime system whenever task-level parallelism leaves cores idle. We present a runtime system for scheduling applications with layered parallelism on Cell and investigate its potential with RAxML, a computational biology application which infers large phylogenetic trees, using the Maximum Likelihood (ML) method. Our experiments show that the Cell benefits significantly from dynamic parallelization methods, that selectively exploit the layers of parallelism in the system, in response to workload characteristics. Our runtime environment outperforms naive parallelization and scheduling based on MPI and Linux by up to a factor of 2.6. We are able to execute RAxML on one Cell four times faster than on a dual-processor system with Hyperthreaded Xeon processors, and 5--10\% faster than on a single-processor system with a dual-core, quad-thread IBM Power5 processor

Observable dynamic compilation

Managed language platforms such as the Java Virtual Machine rely on a dynamic compiler to achieve high performance. Despite the benefits that dynamic compilation provides, it also introduces some challenges to program profiling. Firstly, profilers based on bytecode instrumentation may yield wrong results in the presence of an optimizing dynamic compiler, either due to not being aware of optimizations, or because the inserted instrumentation code disrupts such optimizations. To avoid such perturbations, we present a technique to make profilers based on bytecode instrumentation aware of the optimizations performed by the dynamic compiler, and make the dynamic compiler aware of the inserted code. We implement our technique for separating inserted instrumentation code from base-program code in Oracle's Graal compiler, integrating our extension into the OpenJDK Graal project. We demonstrate its significance with concrete profilers. On the one hand, we improve accuracy of existing profiling techniques, for example, to quantify the impact of escape analysis on bytecode-level allocation profiling, to analyze object life-times, and to evaluate the impact of method inlining when profiling method invocations. On the other hand, we also illustrate how our technique enables new kinds of profilers, such as a profiler for non-inlined callsites, and a testing framework for locating performance bugs in dynamic compiler implementations. Secondly, the lack of profiling support at the intermediate representation (IR) level complicates the understanding of program behavior in the compiled code. This issue cannot be addressed by bytecode instrumentation because it cannot precisely capture the occurrence of IR-level operations. Binary instrumentation is not suited either, as it lacks a mapping from the collected low-level metrics to higher-level operations of the observed program. To fill this gap, we present an easy-to-use event-based framework for profiling operations at the IR level. We integrate the IR profiling framework in the Graal compiler, together with our instrumentation-separation technique. We illustrate our approach with a profiler that tracks the execution of memory barriers within compiled code. In addition, using a deoptimization profiler based on our IR profiling framework, we conduct an empirical study on deoptimization in the Graal compiler. We focus on situations which cause program execution to switch from machine code to the interpreter, and compare application performance using three different deoptimization strategies which influence the amount of extra compilation work done by Graal. Using an adaptive deoptimization strategy, we manage to improve the average start-up performance of benchmarks from the DaCapo, ScalaBench, and Octane suites by avoiding wasted compilation work. We also find that different deoptimization strategies have little impact on steady- state performance

Catecholaminergic Polymorphic Ventricular Tachycardia.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmia syndrome characterised by adenergically mediated bidirectional and/or polymorphic ventricular tachycardia. CPVT is a significant cause of autopsy-negative sudden death in children and adolescents, although it can also affect adults. It is often caused by pathogenic variants in the cardiac ryanodine receptor gene as well as other rarer genes. Early identification and risk stratification is of major importance. β-blockers are the cornerstone of therapy. Sodium channel blockers, specifically flecainide, have an additive role. Left cardiac sympathetic denervation is playing an increasing role in suppression of arrhythmia and symptoms. Concerns have been raised, however, about the efficacy of implantable cardioverter defibrillator therapy and the risk of catecholamine driven proarrhythmic storms. In this review, we summarise the clinical characteristics, genetics, and diagnostic and therapeutic strategies for CPVT and describe recent advances and challenges

Ensuring compliance with data privacy and usage policies in online services

Online services collect and process a variety of sensitive personal data that is subject to complex privacy and usage policies. Complying with the policies is critical, often legally binding for service providers, but it is challenging as applications are prone to many disclosure threats. We present two compliance systems, Qapla and Pacer, that ensure efficient policy compliance in the face of direct and side-channel disclosures, respectively. Qapla prevents direct disclosures in database-backed applications (e.g., personnel management systems), which are subject to complex access control, data linking, and aggregation policies. Conventional methods inline policy checks with application code. Qapla instead specifies policies directly on the database and enforces them in a database adapter, thus separating compliance from the application code. Pacer prevents network side-channel leaks in cloud applications. A tenant’s secrets may leak via its network traffic shape, which can be observed at shared network links (e.g., network cards, switches). Pacer implements a cloaked tunnel abstraction, which hides secret-dependent variation in tenant’s traffic shape, but allows variations based on non-secret information, enabling secure and efficient use of network resources in the cloud. Both systems require modest development efforts, and incur moderate performance overheads, thus demonstrating their usability.Onlinedienste sammeln und verarbeiten eine Vielzahl sensibler persönlicher Daten, die komplexen Datenschutzrichtlinien unterliegen. Die Einhaltung dieser Richtlinien ist häufig rechtlich bindend für Dienstanbieter und gleichzeitig eine Herausforderung, da Fehler in Anwendungsprogrammen zu einer unabsichtlichen Offenlegung führen können. Wir präsentieren zwei Compliance-Systeme, Qapla und Pacer, die Richtlinien effizient einhalten und gegen direkte und indirekte Offenlegungen durch Seitenkanäle schützen. Qapla verhindert direkte Offenlegungen in datenbankgestützten Anwendungen. Herkömmliche Methoden binden Richtlinienprüfungen in Anwendungscode ein. Stattdessen gibt Qapla Richtlinien direkt in der Datenbank an und setzt sie in einem Datenbankadapter durch. Die Konformität ist somit vom Anwendungscode getrennt. Pacer verhindert Netzwerkseitenkanaloffenlegungen in Cloud-Anwendungen. Geheimnisse eines Nutzers können über die Form des Netzwerkverkehr offengelegt werden, die bei gemeinsam genutzten Netzwerkelementen (z. B. Netzwerkkarten, Switches) beobachtet werden kann. Pacer implementiert eine Tunnelabstraktion, die Geheimnisse im Netzwerkverkehr des Nutzers verbirgt, jedoch Variationen basier- end auf nicht geheimen Informationen zulässt und eine sichere und effiziente Nutzung der Netzwerkressourcen in der Cloud ermöglicht. Beide Systeme erfordern geringen Entwicklungsaufwand und verursachen einen moderaten Leistungsaufwand, wodurch ihre Nützlichkeit demonstriert wird

Cloud Computing cost and energy optimization through Federated Cloud SoS

2017 Fall.Includes bibliographical references.The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks

NetShaper: A Differentially Private Network Side-Channel Mitigation System

The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet applications. However, these protocols cannot prevent network side-channel leaks -- leaks of sensitive information through the sizes and timing of network packets. We present NetShaper, a system that mitigates such leaks based on the principle of traffic shaping. NetShaper's traffic shaping provides differential privacy guarantees while adapting to the prevailing workload and congestion condition, and allows configuring a tradeoff between privacy guarantees, bandwidth and latency overheads. Furthermore, NetShaper provides a modular and portable tunnel endpoint design that can support diverse applications. We present a middlebox-based implementation of NetShaper and demonstrate its applicability in a video streaming and a web service application

Polymorphic computing abstraction for heterogeneous architectures

Integration of multiple computing paradigms onto system on chip (SoC) has pushed the boundaries of design space exploration for hardware architectures and computing system software stack. The heterogeneity of computing styles in SoC has created a new class of architectures referred to as Heterogeneous Architectures. Novel applications developed to exploit the different computing styles are user centric for embedded SoC. Software and hardware designers are faced with several challenges to harness the full potential of heterogeneous architectures. Applications have to execute on more than one compute style to increase overall SoC resource utilization. The implication of such an abstraction is that application threads need to be polymorphic. Operating system layer is thus faced with the problem of scheduling polymorphic threads. Resource allocation is also an important problem to be dealt by the OS. Morphism evolution of application threads is constrained by the availability of heterogeneous computing resources. Traditional design optimization goals such as computational power and lower energy per computation are inadequate to satisfy user centric application resource needs. Resource allocation decisions at application layer need to permeate to the architectural layer to avoid conflicting demands which may affect energy-delay characteristics of application threads. We propose Polymorphic computing abstraction as a unified computing model for heterogeneous architectures to address the above issues. Simulation environment for polymorphic applications is developed and evaluated under various scheduling strategies to determine the effectiveness of polymorphism abstraction on resource allocation. User satisfaction model is also developed to complement polymorphism and used for optimization of resource utilization at application and network layer of embedded systems