DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-dimensional Bilateral Filter

This report explores using GPUs as a platform for performing high performance medical image data processing, specifically smoothing using a 3D bilateral filter, which performs anisotropic, edge-preserving smoothing. The algorithm consists of a running a specialized 3D convolution kernel over a source volume to produce an output volume. Overall, our objective is to understand what algorithmic design choices and configuration options lead to optimal performance of this algorithm on the GPU. We explore the performance impact of using different memory access patterns, of using different types of device/on-chip memories, of using strictly aligned and unaligned memory, and of varying the size/shape of thread blocks. Our results reveal optimal configuration parameters for our algorithm when executed sample 3D medical data set, and show performance gains ranging from 30x to over 200x as compared to a single-threaded CPU implementation

The Relationship Between Per Capita Income, Uninsured Rates, and Cardiovascular Mortality in Georgia Between 1994 – 2016

Background: The association between cardiovascular mortality, per capita income and uninsured rates in Georgia have not been well described. Methods: Cardiovascular mortality rates, per capita income and uninsured rates were obtained for the years 1994-2016, and their relationships were analyzed using univariate and multivariate statistical techniques. Results: In bivariate analysis, a strong inverse relationship between cardiovascular mortality and per capita income (r = -0.917, p \u3c 0.0001) was detected, while bivariate analysis detected no relationship between cardiovascular mortality and uninsured rates. Both per capita income and uninsured rates were negative predictors of cardiovascular mortality in multivariate analysis. Conclusions: Per capita income increases in Georgia were strongly correlated with reductions in cardiovascular mortality. While there was not a linear correlation of healthcare coverage status and cardiovascular mortality, it demonstrated a protective effect in multivariate analysis

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system"

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''

The Effects of Myelin on Macrophage Activation Are Phenotypic Specific via cPLA\u3csub\u3e2\u3c/sub\u3e in the Context of Spinal Cord Injury Inflammation

Spinal cord injury (SCI) produces chronic, pro-inflammatory macrophage activation that impairs recovery. The mechanisms driving this chronic inflammation are not well understood. Here, we detail the effects of myelin debris on macrophage physiology and demonstrate a novel, activation state-dependent role for cytosolic phospholipase-A2 (cPLA2) in myelin-mediated potentiation of pro-inflammatory macrophage activation. We hypothesized that cPLA2 and myelin debris are key mediators of persistent pro-inflammatory macrophage responses after SCI. To test this, we examined spinal cord tissue 28-days after thoracic contusion SCI in 3-month-old female mice and observed both cPLA2 activation and intracellular accumulation of lipid-rich myelin debris in macrophages. In vitro, we utilized bone marrow-derived macrophages to determine myelin’s effects across a spectrum of activation states. We observed phenotype-specific responses with myelin potentiating only pro-inflammatory (LPS + INF-γ; M1) macrophage activation, whereas myelin did not induce pro-inflammatory responses in unstimulated or anti-inflammatory (IL-4; M2) macrophages. Specifically, myelin increased levels of pro-inflammatory cytokines, reactive oxygen species, and nitric oxide production in M1 macrophages as well as M1-mediated neurotoxicity. PACOCF3 (cPLA2 inhibitor) blocked myelin’s detrimental effects. Collectively, we provide novel spatiotemporal evidence that myelin and cPLA2 play an important role in the pathophysiology of SCI inflammation and the phenotype-specific response to myelin implicate diverse roles of myelin in neuroinflammatory conditions

Meaning and Practice of Palliative Care for Hospitalized Older Adults with Life Limiting Illnesses

Objective. To illustrate distinctions and intersections of palliative care (PC) and end-of-life (EOL) services through examples from case-centered data of older adults cared for during a four-year ethnographic study of an acute care hospital palliative care consultation service. Methods. Qualitative narrative and thematic analysis. Results. Description of four practice paradigms (EOL transitions, prognostic uncertainty, discharge planning, and patient/family values and preferences) and identification of the underlying structure and communication patterns of PC consultation services common to them. Conclusions. Consistent with reports by other researchers, study data support the need to move beyond equating PC with hospice or EOL care and the notion that EOL is a well-demarcated period of time before death. If professional health care providers assume that PC services are limited to assisting with and helping patients and families prepare for dying, they miss opportunities to provide care considered important to older individuals confronting life-limiting illnesses

Hybrid Parallelism for Volume Rendering on Large, Multi- and Many-core Systems

With the computing industry trending towards multi- and many-core processors, we study how a standard visualization algorithm, ray-casting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with datasets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance