Thomas D. Uram

GROPHECY: GPU performance projection from CPU code skeletons

We propose GROPHECY, a GPU performance projection framework that can estimate the performance benefit of GPU acceleration without actual GPU programming or hardware. Users need only to skeletonize pieces of CPU code that are targets for GPU acceleration. Code skeletons are automatically transformed in various ways to mimic tuned GPU codes with characteristics resembling real implementations. The synthesized characteristics are used by an existing analytical model to project GPU performance. The cost and benefit of GPU development can then be estimated according to the transformed code skeleton that yields the best projected performance. With GROPHECY, users can leap toward GPU acceleration only when the cost-benefit makes sense. The framework is validated using kernel benchmarks and data-parallel codes in legacy scientific applications. The measured performance of manually tuned codes deviates from the projected performance by 17% in geometric mean.

Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)

Proton computed tomography (pCT) is an imaging modality that has been in development to support targeted dose delivery in proton therapy. It aims to accurately map the distribution of relative stopping power. Because protons traverse material media in non-linear paths, pCT requires individual proton processing. Image reconstruction then becomes a time-consuming process. Clinical-use scenarios that require images from billions of protons in less than ten or fifteen minutes have motivated us to use distributed and hardware-accelerated computing methods to achieve fast image reconstruction. Combined use of MPI and GPUs demonstrates that clinically viable image reconstruction is possible. On a 60-node CPU/GPU computer cluster, we achieved efficient strong and weak scaling when reconstructing images from two billion histories in under seven minutes. This represents a significant improvement over the previous state-of-the-art in pCT, which took almost seventy minutes to reconstruct an image from 131 million histories on a single-CPU, single-GPU computer.

Accelerating science gateway development with Web 2.0 and Swift

A Science Gateway is a computational web portal that enables scientists to run scientific simulations, data analysis, and visualization through their web browsers. The major problem of building a science gateway on TeraGrid is how to deploy scientific applications rapidly on computational resources and expose these applications as web services to scientists. In this paper we propose a novel science application framework that can greatly accelerate the development cycle of science gateway systems. This framework enables science gateway developers to import their domain-specific scientific workflow scripts and generate Web 2.0 gadgets for running these application workflows and visualizing the output from workflow executions without writing any web related code. By assembling these application-specific gadgets and some common gadgets predefined in the framework for workflow management, developers can easily set up a customized computational science gateway to meet community requirements. We demonstrate the utility of the framework with an example from computational biochemistry.

A Web 2.0-Based Scientific Application Framework

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.

Large-scale parallel visualization of particle-based simulations using point sprites and level-of-detail

Recent large-scale particle-based simulations are generating vast amounts of data posing a challenge to visualization algorithms. One possibility for addressing this challenge is to map particles into a regular grid for volume rendering, which carries the disadvantages of inefficient use of memory and undesired losses of dynamic range. As an alternative, we propose a method to efficiently visualize these massive particle datasets using point rendering techniques with neither loss of dynamic range nor memory overheads. In addition, a hierarchical reorganization of the data is desired to deliver meaningful visual representations of a large number of particles in a limited number of pixels, preserving point locality and also helping achieve interactive frame rates. In this paper, we present a framework for parallel rendering of large-scale particle data sets combining point sprites and z-ordering. The latter is used to create a multi level representation of the data which helps improving frame rates. Performance and scalability are evaluated on a GPU-based visualization cluster, scaling up to 128 GPUs. Results using particle datasets of up to 32 billion particles are shown.

Performance modeling of vl3 volume rendering on GPU-based clusters

This paper presents an analytical model for parallel volume rendering of large datasets using GPU-based clusters. The model is focused on the parallel volume rendering and compositing stages and predicts their performance requiring only a few input parameters. We also present vl3, a novel parallel volume rendering framework for visualization of large datasets. Its performance is evaluated on a GPU-based cluster, weak and strong scaling are studied, and model predictions are validated with experimental results on up to 128 GPUs.

Streaming visualization for collaborative environments

Connecting expensive and scarce visual data analysis resources to end-users is a major challenge today. We describe a flexible mechanism for meeting this challenge based on commodity compression technologies for streaming video. The advantages of this approach include simplified application development, access to generic client components for viewing, and simplified incorporation of improved codecs as they become available. In this paper we report newly acquired experimental results for two different applications being developed to exploit this approach and test its merits. One is based on a new plugin for ParaView that adds video streaming cleanly and transparently to existing applications. The other is a custom volume rendering application with new remote capabilities. Using typical datasets under realistic conditions, we find the performance for both is satisfactory.

PDACS: a portal for data analysis services for cosmological simulations

Accessing and analyzing data from cosmological simulations is a major challenge due to the prohibitive size of cosmological datasets and the diversity of the associated large-scale analysis tasks. Analysis of the simulated models requires direct access to the datasets, considerable compute infrastructure, and storage capacity for the results. Resource limitations can become serious obstacles to performing research on the most advanced cosmological simulations. The Portal for Data Analysis services for Cosmological Simulations (PDACS) is a web-based workflow service and scientific gateway for cosmology. The PDACS platform provides access to shared repositories for datasets, analytical tools, cosmological workflows, and the infrastructure required to perform a wide variety of analyses. PDACS is a repurposed implementation of the Galaxy workflow engine and supports a rich collection of cosmology-specific datatypes and tools. The platform leverages high-performance computing infrastructure at the National Energy Research Scientific Computing Center (NERSC) and Argonne National Laboratory (ANL), enabling researchers to deploy computationally intensive workflows. In this paper we present PDACS and discuss the process and challenges of developing a research platform for cosmological research.

The Problem Solving Environments of TeraGrid, Science Gateways, and the Intersection of the Two

Problem solving environments (PSEs) are increasingly important for scientific discovery. Todays most challenging problems often require multi-disciplinary teams, the ability to analyze very large amounts of data, and the need to rely on infrastructure built by others rather than reinventing solutions for each science team. The TeraGrid Science Gateways program recognizes these challenges and works with science teams to harness high-end resources that significantly extend a PSEs functionality.

Streaming ultra high resolution images to large tiled display at nearly interactive frame rate with vl3

Visualization of large-scale simulations running on supercomputers requires ultra-high resolution images to capture important features in the data. In this work, we present a system for streaming ultra-high resolution images from a visualization cluster to a remote tiled display at nearly interactive frame rates. vl3, a modular framework for large scale data visualization and analysis, provides the backbone of our implementation. With this system we are able to stream over the network volume renderings of a 20483 voxel dataset at a resolution of 6144×3072 pixels with a frame rate of approximately 3.3 frames per second.