An evaluation of the TRIPS computer system

The TRIPS system employs a new instruction set architecture (ISA) called Explicit Data Graph Execution (EDGE) that renegotiates the boundary between hardware and software to expose and exploit concurrency. EDGE ISAs use a block-atomic execution model in which blocks are composed of dataflow instructions. The goal of the TRIPS design is to mine concurrency for high performance while tolerating emerging technology scaling challenges, such as increasing wire delays and power consumption. This paper evaluates how well TRIPS meets this goal through a detailed ISA and performance analysis. We compare performance, using cycles counts, to commercial processors. On SPEC CPU2000, the Intel Core 2 outperforms compiled TRIPS code in most cases, although TRIPS matches a Pentium 4. On simple benchmarks, compiled TRIPS code outperforms the Core 2 by 10% and hand-optimized TRIPS code outperforms it by factor of 3. Compared to conventional ISAs, the block-atomic model provides a larger instruction window, increases concurrency at a cost of more instructions executed, and replaces register and memory accesses with more efficient direct instruction-to-instruction communication. Our analysis suggests ISA, microarchitecture, and compiler enhancements for addressing weaknesses in TRIPS and indicates that EDGE architectures have the potential to exploit greater concurrency in future technologies

Synoptic-scale controls on the δ18O in precipitation across Beringia

Oxygen isotope records of precipitation (δ18Oprecip) from Beringia are thought to reflect synoptic-scale circulation changes associated with the Aleutian Low. To delineate the spatial pattern of δ18Oprecip associated with the two dominant modes of Aleutian Low circulation, we combine modern δ18Oprecip and deuterium excess data with climate reanalysis and back-trajectory modelling. Aleutian Low strength and position are revealed to systematically affect regional moisture source and δ18Oprecip; whereby a strengthened Aleutian Low causes lower (higher) δ18Oprecip in western (eastern) Beringia. We compare a new 100-year-long δ18O record from the Aleutian Islands with the North Pacific Index, the primary indicator of Aleutian Low strength, and find a significant positive relationship (r = 0.43, p < 0.02, n = 28) that tracks late 20th century change. This study demonstrates synoptic-scale circulation controls on our isotope record, and provides a coherent framework for interpreting existing and emerging paleo-isotope data from the region

Prospective Observational Study on acute Appendicitis Worldwide (POSAW)

Acute appendicitis (AA) is the most common surgical disease, and appendectomy is the treatment of choice in the majority of cases. A correct diagnosis is key for decreasing the negative appendectomy rate. The management can become difficult in case of complicated appendicitis. The aim of this study is to describe the worldwide clinical and diagnostic work-up and management of AA in surgical departments.info:eu-repo/semantics/publishedVersio

Non-Gaussian clutter simulation and distribution approximation using spherically invariant random vectors

Conventional radar receivers are based on the assumption of Gaussian distributed clutter. In a non-homogeneous environment and as the resolution capabilities of radar systems improve, the validity of this assumption becomes questionable and the clutter is often observed to be non-Gaussian. For example, the Weibull and K-distributions have been shown to approximate some experimentally measured non-Gaussian clutter data. In this environment the detection performance of the Gaussian receiver may be significantly below that of the optimum non-Gaussian receiver. In order to obtain improved detection performance, it is necessary to characterize the correlated non-Gaussian clutter samples. This characterization by means of spherically invariant random vectors (SIRVs) is the primary focus of this dissertation. Although SIRVs have been previously investigated and appear to be an appropriate model for the non-Gaussian clutter, many questions remain. Can the library of known SIRVs be expanded? Can efficient techniques be developed for computer generation? Given random data suitably modeled by an SIRV, how effectively can the unknown distribution be approximated? The answers to these and related questions are addressed in this dissertation. In particular, an enlarged library of distributions that conform to the SIRV model is presented, as well as efficient techniques for their computer generation. A technique for approximating an unknown univariate distribution from a small sample of experimental data, the Öztürk algorithm, is extended to the multivariate case for SIRVs. In this approach the envelope of the SIRV is used to reduce the multivariate distribution approximation problem to an equivalent univariate approximation problem. Problems are encountered, however, in the need to normalize the SIRV distributions with respect to their covariance matrix. It is difficult to estimate the covariance matrix when the type of SIRV distribution is unknown. Therefore, the sample covariance matrix is used. A key result of the dissertation is that the resulting error can be incorporated into the approximation process, thereby reducing its impact. Once the clutter distribution has been approximated, the covariance matrix can then be re-estimated using the approximated distribution. Finally, techniques for approximating an SIRV with multivariate Gaussian mixtures are proposed

A patch memory system for image processing and computer vision

From self-driving cars to high dynamic range (HDR) imaging, the demand for image-based applications is growing quickly. In mobile systems, these applications place particular strain on performance and energy efficiency. As traditional memory systems are optimized for 1D memory access, they are unable to efficiently exploit the multi-dimensional locality characteristics of image-based applications which often operate on sub-regions of 2D and 3D image data. We have developed a new Patch Memory System (PMEM) tailored to application domains that process 2D and 3D data streams. PMEM supports efficient multidimensional addressing, automatic handling of image boundaries, and efficient caching and prefetching of image data. In addition to an optimized cache, PMEM includes hardware for offloading structured address calculations from processing units. We improve average energy-delay by 26% compared to EVA, a memory system for computer vision applications. Compared to a traditional cache, our results show that PMEM can reduce processor energy by 34% for a selection of CV and IP applications, leading to system performance improvement of up to 32% and energy-delay product improvement of 48-86% on the applications in this study