Multi-focus microscope (MFM) provides a way to obtain 3D information by simultaneously capturing multiple focal planes. The naive method for MFM reconstruction is to stack the sub-images with alignment. However, the resolution in the z-axis in this method is limited by the number of acquired focal planes. In this work we build on a recent reconstruction algorithm for MFM, using information from multiple frames to improve the reconstruction quality. We propose two multiple-frame MFM image reconstruction algorithms: batch and recursive approaches. In the batch approach, we take multiple MFM frames and jointly estimate the 3D image and the motion for each frame. In the recursive approach, we utilize the reconstructed image from the previous frame. Experimental results show that the proposed algorithms produce a sequence of 3D object reconstruction with high quality that enable reconstruction of dynamic extended objects.

We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates. However, each iteration of IPM is more expensive than in ISTA because it needs to evaluate an inverse of a Hessian matrix to compute the Newton direction. We propose to approximate each Hessian matrix by a diagonal matrix plus a rank-one matrix. This approximation matrix is easily invertible using the Sherman-Morrison formula, and is used as a novel preconditioner in a preconditioned conjugate gradient method to compute a truncated Newton direction. We demonstrate the efficiency of our algorithm in compressive 3D volumetric image reconstruction. Numerical experiments show favorable results of our method in comparison with previous interior point based and iterative shrinkage/thresholding based algorithms.

We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z-axis, but the result suffers from poor resolution in the z-axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.

We present a computer vision system that can transcribe the text on tiny printed labels stacked beneath pinned insects (as found in museum collections). The approach uses multiple views of each label because the labels are often occluded by the pin, the insect specimen, and other labels. Our approach handles occlusion and the extreme viewing angles required to image the stacked labels. Automated image analysis identifies the lines of text and then aligns and rectifies the images. Combining the aligned and rectified images from multiple viewpoints enables us to create a composite image that can be read using optical character recognition tools (OCR) to extract the text. We provide experimental demonstration using both museum specimens and experimental test labels.

This paper presents the design and prototyping of hardware and software to address the problem of rapid and reliable 3D digitization of very large collections of pinned insects. Using the collection at the Field Museum of Natural History (FMNH) as a use case, a pipeline to ingest the entire collection of 4.5 million specimens in circa 1-2 years imposes a few second limit on average processing time per specimen. We describe the design and implementation of multi-camera systems capable of rapidly capturing light field imagery for 3D reconstruction of label surfaces and specimen in single snapshots consistent with this time constraint. With imagery captured using these prototype multi-cameras we demonstrate methods under development for 3D reconstruction of pinned insect specimens and for processing text on label surfaces.

Today's leadership computing facilities have enabled the execution of transformative simulations at unprecedented scales. However, analyzing the huge amount of output from these simulations remains a challenge. Most analyses of this output is performed in post-processing mode at the end of the simulation. The time to read the output for the analysis can be significantly high due to poor I/O bandwidth, which increases the end-to-end simulation-analysis time. Simulation-time analysis can reduce this end-to-end time. In this work, we present the scheduling of in-situ analysis as a numerical optimization problem to maximize the number of online analyses subject to resource constraints such as I/O bandwidth, network bandwidth, rate of computation and available memory. We demonstrate the effectiveness of our approach through two application case studies on the IBM Blue Gene/Q system.

There is growing concern that I/O systems will be hard pressed to satisfy the requirements of future leadership-class machines. Even current machines are found to be I/O bound for some applications. In this paper, we identify existing performance bottlenecks in data movement for I/O on the IBM Blue Gene/P (BG/P) supercomputer currently deployed at several leadership computing facilities. We improve the I/O performance by exploiting the network topology of BG/P for collective I/O, leveraging data semantics of applications and incorporating asynchronous data staging. We demonstrate the efficacy of our approaches for synthetic benchmark experiments and for application-level benchmarks at scale on leadership computing systems.

A significant obstacle to building usable, web-based interfaces for computational science in a Grid environment is how to deploy scientific applications on computational resources and expose these applications as web services. To streamline the development of these interfaces, we propose a new application framework that can deliver user-defined scientific workflows as both web services and OpenSocial gadgets. Through this application framework, scientists can focus on defining computational workflows using domain-specific applications and can use the software tools in the framework to quickly generate gadgets for running the applications and visualizing the output from workflow executions. By assembling these domain-specific gadgets and some common gadgets predefined in the framework for workflow management, scientists can easily set up a customized computational workspace to meet their requirements.

Current leadership-class machines suffer from a significant imbalance between their computational power and their I/O bandwidth. I/O forwarding is a paradigm that attempts to bridge the increasing performance and scalability gap between the compute and I/O components of leadership-class machines to meet the requirements of data-intensive applications by shipping I/O calls from compute nodes to dedicated I/O nodes. I/O forwarding is a critical component of the I/O subsystem of the IBM Blue Gene/P supercomputer currently deployed at several leadership computing facilities. In this paper, we evaluate the performance of the existing I/O forwarding mechanisms for BG/P and identify the performance bottlenecks in the current design. We augment the I/O forwarding with two approaches: I/O scheduling using a work-queue model and asynchronous data staging. We evaluate the efficacy of our approaches using microbenchmarks and application-level benchmarks on leadership class systems.

This paper proposes the study of a new computation model that attempts to address the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. In this paper, we present the progress of our research on a parallel programming and execution model - mainly, ParalleX. We describe the functional elements of ParalleX, one such model being explored as part of this project. We also report our progress on the development and study of a subset of ParalleX $the LITL-X at University of Delaware. We then present a novel architecture model - Gilgamesh II - as a ParalleX processing architecture. A design point study of Gilgamesh II and the architecture concept strategy are presented.

This paper addresses the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. We are developing a hierarchical threaded virtual machine (HTVM) that defines a dynamic, multithreaded execution model and programming model, providing an architecture abstraction for HEC system software and tools development. We are working on a prototype language, LITL-X (pronounced "little-X") for latency intrinsic-tolerant language, which provides the application programmers with a powerful set of semantic constructs to organize parallel computations in a way that hides/manages latency and limits the effects of overhead. This is quite different from locality management, although the intent of both strategies is to minimize the effect of latency on the efficiency of computation. We work on a dynamic compilation and runtime model to achieve efficient LITL-X program execution. Several adaptive optimizations were studied. A methodology of incorporating domain-specific knowledge in program optimization was studied. Finally, we plan to implement our method in an experimental testbed for a HEC architecture and perform a qualitative and quantitative evaluation on selected applications

Demand for high resolution visualization, large pixel real estate collaborative workspaces, and interactive computer interfaces continue to drive researchers to develop new physical portals connecting them to their computational tools, to their data, and to their colleagues around the world. In this paper we describe CupHolder, a high performance workstation designed to support interactive collaboration and research activities. It is configured to enable display of high resolution imagery while enabling a comfortable interactive environment for one to several co-located researchers. Moreover it is driven by a high performance commodity cluster that provides substantial local rendering muscle as well as a high performance interface to Grid-based computational tasks. CupHolder is comprised of commodity components. It is primarily novel because it represents an integration of these components into a new form factor that we believe is a useful precursor and testbed for future integrated workspaces.

Efficient execution of applications requires insight into how the system features impact the performance of the application. For distributed systems, the task of gaining this insight is complicated by the complexity of the system features. This insight generally results from significant experimental analysis and possibly the development of performance models. This paper presents the Prophesy project, an infrastructure that aids in gaining this needed insight based upon experience. The core component of Prophesy is a relational database that allows for the recording of performance data, system features and application details.