high performance computing on graphics processing units: hgpu.org

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 […]

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. […]

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,’ […]

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. […]

Graphics processing units (GPUs) traditionally have been used to accelerate only parts of the graphics pipelines. The emergence of the new age GPUs as highly parallel, multi-threaded and many core processor systems with the ability to perform general purpose computations has opened doors for new form of heterogeneous computing where the GPU and CPU can […]

Sophisticated video processing effects require both image and geometry information. We explore the possibility to augment a video camera with a recent infrared time-of-flight depth camera, to capture high-resolution RGB and low-resolution, noisy depth at video frame rates. To turn such a setup into a practical RGBZ video camera, we develop efficient data filtering techniques […]

GPUs are flexible parallel processors capable of accelerating real applications. To exploit them, programmers must ensure a consistent program state between the CPU and GPU memories by managing data. Manually managing data is tedious and error-prone. In prior work on automatic CPU-GPU data management, alias analysis quality limits performance, and type-inference quality limits applicability. This […]

An important challenge in machine learning is to develop learning algorithms that can handle large amounts of data at a realistically large scale. This entails not only the development of algorithms that can be efficiently trained to infer parameters of the model in a given dataset, but also demands careful thought about the tools (both […]

BACKGROUND: Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl’s Law. This work is the first known to accelerate the third stage of […]

In this paper, we introduce a hardware acceleration technique for the parallel Finite Difference Time Domain (FDTD) method using the SSE (Streaming SIMD (Single Instruction Multiple Data) Extensions) instruction set. The implementation of SSE instruction set to parallel FDTD method has been achieved the significant improvement on the simulation performance. The benchmarks of the SSE […]

We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, […]

The paper presents a new approach to the human reidentification problem using covariance features. In many cases a distance operator between signatures based on generalized eigenvalues has to be computed efficiently, especially once the real-time response is expected from the system. This is a challenging problem as many procedures are computationally intensive tasks and must […]