Remote-scope Promotion: Clarified, Rectified, and Verified

Modern accelerator programming frameworks, such as OpenCL, organise threads into work-groups. Remote-scope promotion (RSP) is a language extension recently proposed by AMD researchers that is designed to enable applications, for the first time, both to optimise for the common case of intra-work-group communication (using memory scopes to provide consistency only within a work-group) and to allow occasional inter-work-group communication (as required, for instance, to support the popular load-balancing idiom of work stealing). We present the first formal, axiomatic memory model of OpenCL extended with RSP. We have extended the Herd memory model simulator with support for OpenCL kernels that exploit RSP, and used it to discover bugs in several litmus tests and a work-stealing queue, that have been used previously in the study of RSP. We have also formalised the proposed GPU implementation of RSP. The formalisation process allowed us to identify bugs in the description of RSP that could result in well-synchronised programs experiencing memory inconsistencies. We present and prove sound a new implementation of RSP that incorporates bug fixes and requires less non-standard hardware than the original implementation. This work, a collaboration between academia and industry, clearly demonstrates how, when designing hardware support for a new concurrent language feature, the early application of formal tools and techniques can help to prevent errors, such as those we have found, from making it into silicon

Disengaged Scheduling for Fair, Protected Access to Fast Computational Accelerators

Today’s operating systems treat GPUs and other computational accelerators as if they were simple devices, with bounded and predictable response times. With accelerators assuming an increasing share of the workload on modern machines, this strategy is already problematic, and likely to become untenable soon. If the operating system is to enforce fair sharing of the machine, it must assume responsibility for accelerator scheduling and resource management. Fair, safe scheduling is a particular challenge on fast accelerators, which allow applications to avoid kernel-crossing overhead by interacting directly with the device. We propose a disengaged scheduling strategy in which the kernel intercedes between applications and the accelerator on an infrequent basis, to monitor their use of accelerator cycles and to determine which applications should be granted access over the next time interval. Our strategy assumes a well defined, narrow interface exported by the accelerator. We build upon such an interface, systematically inferred for the latest Nvidia GPUs. We construct several example schedulers, including Disengaged Timeslice with overuse control that guarantees fairness and Disengaged Fair Queueing that is effective in limiting resource idleness, but probabilistic. Both schedulers ensure fair sharing of the GPU, even among uncooperative or adversarial applications; Disengaged Fair Queueing incurs a 4 % overhead on average (max 18%) compared to direct devic

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources

Demography and disorders of German Shepherd Dogs under primary veterinarycare in the UK

The German Shepherd Dog (GSD) has been widely used for a variety of working roles. However, concerns for the health and welfare of the GSD have been widely aired and there is evidence that breed numbers are now in decline in the UK. Accurate demographic and disorder data could assist with breeding and clinical prioritisation. The VetCompassTM Programme collects clinical data on dogs under primary veterinary care in the UK. This study included all VetCompassTM dogs under veterinary care during 2013. Demographic, mortality and clinical diagnosis data on GSDs were extracted and reported

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions

Impairment of germline transmission after blastocyst injection with murine embryonic stem cells cultured with mouse hepatitis virus and mouse minute virus

The aim of this study was to determine the susceptibility of murine embryonic stem (mESCs) to mouse hepatitis virus (MHV-A59) and mouse minute virus (MMVp) and the effect of these viruses on germline transmission (GLT) and the serological status of recipients and pups. When recipients received 10 blastocysts, each injected with 100 TCID50 MHV-A59, three out of five recipients and four out of 14 pups from three litters became seropositive. When blastocysts were injected with 10−5 TCID50 MMVp, all four recipients and 14 pups from four litters remained seronegative. The mESCs replicated MHV-A59 but not MMVp, MHV-A59 being cytolytic for mESCs. Exposure of mESCs to the viruses over four to five passages but not for 6 h affected GLT. Recipients were seropositive for MHV-A59 but not for MMVp when mESCs were cultured with the virus over four or five passages. The data show that GLT is affected by virus-contaminated mESCs

A Synthetic Uric Acid Analog Accelerates Cutaneous Wound Healing in Mice

Wound healing is a complex process involving intrinsic dermal and epidermal cells, and infiltrating macrophages and leukocytes. Excessive oxidative stress and associated inflammatory processes can impair wound healing, and antioxidants have been reported to improve wound healing in animal models and human subjects. Uric acid (UA) is an efficient free radical scavenger, but has a very low solubility and poor tissue penetrability. We recently developed novel UA analogs with increased solubility and excellent free radical-scavenging properties and demonstrated their ability to protect neural cells against oxidative damage. Here we show that the uric acid analog (6, 8 dithio-UA, but not equimolar concentrations of UA or 1, 7 dimethyl-UA) modified the behaviors of cultured vascular endothelial cells, keratinocytes and fibroblasts in ways consistent with enhancement of the wound healing functions of all three cell types. We further show that 6, 8 dithio-UA significantly accelerates the wound healing process when applied topically (once daily) to full-thickness wounds in mice. Levels of Cu/Zn superoxide dismutase were increased in wound tissue from mice treated with 6, 8 dithio-UA compared to vehicle-treated mice, suggesting that the UA analog enhances endogenous cellular antioxidant defenses. These results support an adverse role for oxidative stress in wound healing and tissue repair, and provide a rationale for the development of UA analogs in the treatment of wounds and for modulation of angiogenesis in other pathological conditions