Mapping massive SIMD parallelism onto vector architectures for simulation

A software behavioural simulator for a new massively parallel single-instruction/multiple data (SIMD) architecture has been developed that can accurately simulate the entire 16, 384 bit-serial processor array. The key to this high performance modelling is the exploitation of an inherent mapping that exists between massively parallel SIMD architectures and the vector architectures used in many high performance scientific super-computers. The new SIMD architecture, called BLITZEN, is based on the Massively Parallel Processor (MPP) built for NASA by Goodyear in the late 1970s. By simulating the full-scale machine with very high performance, the simulator allows development of algorithms and high-level software to proceed before realization of the hardware. This paper describes the SIMD - vector architecture mapping, the highly vectorized simulator in which it is used, and how the result was a simulator that achieved a level of performance three orders of magnitude faster than the conventional uniprocessor approach.     

Recent Advances in Projection Mapping Algorithms,Hardware and Applications

This State‐of‐the‐Art‐Report covers the recent advances in research fields related to projection mapping applications. We summarize the novel enhancements to simplify the 3D geometric calibration task, which can now be reliably carried out either interactively or automatically using self‐calibration methods. Furthermore, improvements regarding radiometric calibration and compensation as well as the neutralization of global illumination effects are summarized. We then introduce computational display approaches to overcome technical limitations of current projection hardware in terms of dynamic range, refresh rate, spatial resolution, depth‐of‐field, view dependency, and color space. These technologies contribute towards creating new application domains related to projection‐based spatial augmentations. We summarize these emerging applications, and discuss new directions for industries.     

Comparisons of air traffic control implementations on an associative processor with a MIMD and consequences for parallel computing

This paper has two complementary focuses. The first is the system design and algorithmic development for air traffic control (ATC) using an associative SIMD processor (AP). The second is the comparison of this implementation with a multiprocessor implementation and the implications of these comparisons. This paper demonstrates how one application, ATC, can more easily, more simply, and more efficiently be implemented on an AP than is generally possible on other types of traditional hardware. The AP implementation of ATC will take advantage of its deterministic hardware to use static scheduling. The software will be dramatically smaller and cheaper to create and maintain. Likewise, a large AP system will be considerably simpler and cheaper than the MIMD hardware currently used. While APs were used for ATC-type applications earlier, these are no longer available. We use a ClearSpeed CSX600 accelerator to emulate the AP solutions of ATC on an ATC prototype consisting of eight data-intensive ATC real-time tasks. Its performance is compared with an 8-core multiprocessor (MP) using OpenMP. Our extensive experiments show that the AP implementation meets all deadlines while the MP will regularly miss a large number of deadlines. The AP code will be similar in size to sequential code for the same tasks and will avoid all of the additional support software needed with an MP to handle dynamic scheduling, load balancing, shared resource management, race conditions, false sharing, etc. At this point, essentially only MIMD systems are built. Many of the advantages of using an AP to solve an ATC problem would carry over to other applications. AP solutions for a wide variety of applications will be cited in this paper. Applications that involve a high degree of data parallelism such as database management, text processing, image processing, graph processing, bioinformatics, weather modeling, managing UAS (Unmanned Aircraft Systems or drones) etc., are good candidates for AP solutions. This raises the issue of whether we should routinely consider using non-multiprocessor hardware like the AP for applications where substantially simpler software solutions will normally exist. It also raises the question of whether the use of both AP and MIMD hardware in a single hetergeneous system could provide more versatility and efficiency. Either the AP or MIMD could serve as the primary system, but could hand off jobs it could not handle efficiently to the other system.