Locality-Conscious Load Balancing: Connectionist Architectural Support

Traditionally, in distributed memory architectures, locality maintenance and load balancing are seen as user level activities involving compiler and runtime system support in software. Such software solutions require an explicit phase of execution, requiring the application to suspend its activities. This paper presents the first (to our knowledge) architecture-level scheme for extracting locality concurrent with the application execution. An artificial neural network coprocessor is used for dynamically monitoring processor reference streams to learn temporally emergent utilities of data elements in ongoing local computations. This facilitates use of kernel-level load balancing schemes thus, easing the user programming burden. The kernel-level scheme migrates data to processor memories evincing higher utilities during load-balancing. The performance of an execution-driven simulation evaluating the proposed coprocessor is presented for three applications. The applications chosen represent the range of load and locality fluxes encounted in parallel programs, with (a) static locality and load characteristics, (b) slowly varying localities for fixed datasetsizes and (c) rapidly fluctuating localities among slowly varying datasetsizes. The performance results indicate the viability and success of the coprocessor in concurrently extracting locality for use in load balancing activities

GST Networks: Learning Emergent Spatiotemporal Correlations1

This paper presents two novel (GIST and GEST) networks, which combine unsupervised featureextraction and Hebbian learning, for tracking emergent correlations in the evolution of spatiotemporal distributions. The networks were successfully tested on the challenging Data Mapping problem, using an execution driven simulation of their implementation in hardware.

Locality-Conscious Load Balancing: Connectionist Architectural Support

Traditionally, in distributed memory architectures, locality maintenance and load balancing are seen as user level activities involving compiler and runtime system support in software. Such software solutions require an explicit phase of execution, requiring the application to suspend its activities. This paper presents the first (to our knowledge) architecture-level scheme for extracting locality concurrent with the application execution. An artificial neural network coprocessor is used for dynamically monitoring processor reference streams to learn temporally emergent utilities of data elements in ongoing local computations. This facilitates use of kernel-level load balancing schemes thus, easing the user programming burden. The kernel-level scheme migrates data to processor memories evincing higher utilities during load-balancing. The performance of an execution-driven simulation evaluating the proposed coprocessor is presented for three applications. The applications chosen represe..

Endovascular stent graft repair of abdominal aortic aneurysms in high-risk patients: a single center experience

BACKGROUND: Endovascular stent graft (EVG) repair can be a safe alternative to open surgical repair to treat abdominal aortic aneurysms (AAA) in high-risk patients. We report our results with EVG repair in such high-risk patients at our institution. OBJECTIVES: We wanted to show that EVG repair can be performed successfully and with a low complication rate in patients with serious comorbidities. METHODS: All patients prospectively studied underwent EVG repair of AAA from February 2000 to July 2002. RESULTS: Of the 60 patients studied, 45 (75%) were high-risk surgical candidates because of associated comorbidities; their aneurysms ranged from 4.5 to 10 cm (mean: 5.7 +/- 1.2 cm). Fifty-nine of 60 patients (98.3%) were treated successfully. Two (3.3%) who underwent surgical intervention for site-related complications died from postoperative complications. Hospital stay was(77%) patients. CONCLUSION: Our preliminary results show that EVG is safe, feasible, and yields excellent technical success even in patients at high risk for complications. Teamwork between interventional cardiologists and vascular surgeons is advised

Acute suppurative bacterial dacryoadenitis: a case series

BACKGROUND: We present a series of patients with acute suppurative bacterial dacryoadenitis and review the clinical presentation, microbiology, treatment options and outcome. METHODS: A multicentre, retrospective, case series review of patients with a clinical diagnosis of acute bacterial suppurative dacryoadenitis (ASBD). Records were examined to obtain information regarding patient demographics, presenting symptoms and signs, radiology, microbiology, management, outcomes and follow-up. RESULTS: 11 patients (9 men, 2 women; mean age 43.9 years, range: 6-82 years) were included. Average time to presentation was 2.8 days, and predisposing conditions were found in 45% of cases. Common presenting symptoms were eyelid swelling, pain, redness and diplopia, and common signs were ptosis, discharge and restriction of eye movements. The most common causative bacteria were Staphylococcus aureus and skin flora. Lacrimal gland swelling was universally seen on CT, with globe indentation of displacement in 27% of cases. Intravenous antibiotics were used in 91% of cases, which subsequently resolved over an average period of 9.7 days. Those with abscess formation (n=2) required incision and drainage. CONCLUSIONS: ASBD is a rare condition that resolves quickly if managed appropriately. Underlying anatomical, infectious or inflammatory conditions should be investigated, and skin commensals should be covered with the instigation of antibiotic therapy.Lucy A Goold, Simon N Madge, Alicia Au, Igal Leibovitch, Alan McNab, Krishna Tumuluri, Dinesh Selv