Stephen Junkins

Larrabee: A Many-Core x86 Architecture for Visual Computing

The Larrabee many-core visual computing architecture uses multiple in-order x86 cores augmented by wide vector processor units, together with some fixed-function logic. This increases the architectures programmability as compared to standard GPUs. The article describes the Larrabee architecture, a software renderer optimized for it, and other highly parallel applications. The article analyzes performance through scalability studies based on real-world workloads.

Augmented Reality System

In recent years, Augmented Reality (AR)[1][2][3] is very popular in universities and research organizations. The AR technology has been widely used in Virtual Reality (VR) fields, such as sophisticated weapons, flight vehicle development, data model visualization, virtual training, entertainment and arts. AR has characteristics to enhance the display output as a real environment with specific user interactive functions or specific object recognitions. It can be use in medical treatment, anatomy training, precision instrument casting, warplane guidance, engineering and distance robot control. AR has a lot of vantages than VR. This system developed combines sensors, software and imaging algorithms to make users feel real, actual and existing. Imaging algorithms include gray level method, image binarization method, and white balance method in order to make accurate image recognition and overcome the effects of light.

Soft irregular shadow mapping: fast, high-quality, and robust soft shadows

We introduce a straightforward, robust, and efficient algorithm for rendering high-quality soft shadows in dynamic scenes. Each frame, points in the scene visible from the eye are inserted into a spatial acceleration structure. Shadow umbrae are computed by sampling the scene from the light at the image plane coordinates given by the stored points. Penumbrae are computed at the same set of points, per silhouette edge, in two steps. First, the set of points affected by a given edge is estimated from the expected light-view screen-space bounds of the corresponding penumbra. Second, the actual overlap between these points and the penumbra is computed analytically directly from the occluding geometry. The umbral and penumbral sources of occlusion are then combined to determine the degree of shadow at the eye-view pixel corresponding to each sample point. An implementation of this algorithm for the Larrabee architecture yields from 27 to 33 frames per second in simulation for scenes from a modern game, and produces significantly higher image quality than other recent methods in the real-time domain.

Color representation in virtual environments

Techniques are suggested for improved color treatment in real-time rendering of synthetic environments. These include the use of emissive textures within a radiosity-based, global illumination model, the addition of dynamic specular highlights to a radiosity-based model, and an alternative approach to full spectral representation that uses a radiosity solution engine in the complex domain. The target application, dynamic rendering of theatrical settings for lighting design, demands both color accuracy and correct treatment of subtle reflections, translucency, and specular highlights. The new techniques appear to provide an appropriate solution.

Exploiting integrated GPUs for network packet processing workloads

Software-based network packet processing on standard high volume servers promises better flexibility, manageability and scalability, thus gaining tremendous momentum in recent years. Numerous research efforts have focused on boosting packet processing performance by offloading to discrete Graphics Processing Units (GPUs). While integrated GPUs, residing on the same die with the CPU, offer many advanced features such as on-chip interconnect CPU-GPU communication, and shared physical/virtual memory, their applicability for packet processing workloads has not been fully understood and exploited. In this paper, we conduct in-depth profiling and analysis to understand the integrated GPUs capabilities and performance potential for packet processing workloads. Based on that understanding, we introduce a GPU accelerated network packet processing framework that fully utilizes integrated GPUs massive parallel processing capability without the need for large numbers of packet batching, which might cause a significant processing delay. We implemented the proposed framework and evaluated the performance with several common, light-weight packet processing workloads on the Intel® Xeon® Processor E3-1200 v4 product family (codename Broadwell) with an integrated GT3e GPU. The results show that our GPU accelerated packet processing framework improved the throughput performance by 2-2.5x, compared to optimized packet processing on CPU only.