Posts
Jan, 21
EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model
Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R&D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend […]
Jan, 21
Plenoptic Rendering With Interactive Performance Using GPUs
Processing and rendering of plenoptic camera data requires significant computational power and memory bandwidth. At the same time, real-time rendering performance is highly desirable so that users can interactively explore the infinite variety of images that can be rendered from a single plenoptic image. In this paper we describe a GPU-based approach for lightfield processing […]
Jan, 21
Direct Visualization of Particle-Partition of Unity Data
Direct visualization of higher-order data provides manifold advantages over the traditional approach, which is based on resampling and subsequent visualization by interpolation-based techniques. Most important, it avoids excessive computation and consumption of memory, and prevents artifacts by pixel-accurate visualization at interactive rates. This work addresses particle-partition of unity simulation data, where fields are modeled both […]
Jan, 21
The State of the Art in Interactive Global Illumination
The interaction of light and matter in the world surrounding us is of striking complexity and beauty. Since the very beginning of computer graphics, adequate modeling of these processes and efficient computation is an intensively studied research topic and still not a solved problem. The inherent complexity stems from the underlying physical processes as well […]
Jan, 21
Fast Graph Cuts using Shrink-Expand Reparameterization
Global optimization of MRF energy using graph cuts is widely used in computer vision. As the images are getting larger, faster graph cuts are needed without sacrificing optimality. Initializing or reparameterizing a graph using results of a similar one has provided efficiency in the past. In this paper, we present a method to speedup graph […]
Jan, 20
On the Correctness of the SIMT Execution Model of GPUs
GPUs are becoming a primary resource of computing power. They use a single instruction, multiple threads (SIMT) execution model that executes batches of threads in lockstep. If the control flow of threads within the same batch diverges, the different execution paths are scheduled sequentially; once the control flows reconverge, all threads are executed in lockstep […]
Jan, 20
Automated Techniques for Enabling Efficient MPI Application Migration
Applications that use the MPI standard have additional dependencies related to the MPI implementation. When migrating an MPI code to a new computing site, the binary will not run if these dependencies are not resolved by properly configuring the new site. In this work, we present techniques that automatically resolve dependencies before runtime and enable […]
Jan, 20
Experiences in Teaching a Specialty Multicore Computing Course
We detail the design and experiences in delivering a specialty multicore computing course whose materials are openly available. The course ambitiously covers three multicore programming paradigms: shared memory (OpenMP), device (CUDA) and message passing (RCCE), and involves significant practical work on their respective platforms: an UltraSPARC T2, Fermi GPU and the Single-Chip Cloud Computer. Specialized […]
Jan, 20
Stochastic Progressive Photon Mapping for Dynamic Scenes
Stochastic Progressive Photon Mapping (SPPM) is a method to simulate consistent global illumination. It is especially useful for complicated light paths like caustics seen through a glass surface. Up to now, SPPM can only be applied to a static scene and noise-free images require hours to compute. Our approach is to extend this method to […]
Jan, 20
An Energy-Efficient Heterogeneous System for Embedded Learning and Classification
Embedded learning applications in automobiles, surveillance, robotics, and defense are computationally intensive, and process large amounts of real-time data. Systems for such workloads have to balance stringent performance constraints within limited power budgets. High performance computer processing units (CPUs) and graphics processing units (GPUs) cannot be used in an embedded platform due to power issues. […]
Jan, 20
Parallel Volume Rendering for Large Scientific Data
Data sets of immense size are regularly generated by large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a ‘visualization cluster,’ […]
Jan, 20
Leveraging on High-Performance Computing and Cloud Technologies in Digital Libraries: A Case Study
With the emergence of high-performance computing instances in the cloud, massive scale computations have become available to technically every organization. Digital libraries typically employ a data-intensive infrastructure, but given the resources, advanced services based on data and text mining could be developed. A fundamental issue is the ease of development and integration of such services. […]