Issues using the Nexus Interface for Measurement-Based WCET Analysis

Hardware debug interfaces such as Nexus have the power to unleash the full potential of measurement-based WCET approaches due to the passive nature in which timing data are collected from the processor. However, difficulties arise as a result of their restrictive nature, thus disallowing true user freedom in the selection of instrumenta- tion point placement. This paper elaborates on the problems encountered when using the Nexus inter- face in our measurement-based WCET framework, and how some of these issues can be resolved, par- ticularly that of irreducibility

Hybrid measurement-based WCET analysis at the source level using object-level traces

Hybrid measurement-based approaches to worst-case execution time (WCET) analysis combine measured execution times of small program segments using static analysis of the larger software structure. In order to make the necessary measurements, instrumentation code is added to generate a timestamped trace from the running program. The intrusive presence of this instrumentation code incurs a timing penalty, widely referred to as the probe effect. However, recent years have seen the emergence of trace capability at the hardware level, effectively opening the door to probe-free analysis. Relying on hardware support forces the WCET analysis to the object-code level, since that is all that is known by the hardware. A major disadvantage of this is that it is expensive for a typical software engineer to interpret the results, since most engineers are familiar with the source code but not the object code. Meaningful WCET analysis involves not just running a tool to obtain an overall WCET value but also understanding which sections of code consume most of the WCET in order that corrective actions, such as optimisation, can be applied if the WCET value is too large. The main contribution of this paper is a mechanism by which hybrid WCET analysis can still be performed at the source level when the timestamped trace has been collected at the object level by state-of-the-art hardware. This allows existing, commercial tools, such as rapitime{}, to operate without the need for intrusive instrumentation and thus without the probe effect

The Detergent Evaluation Methods and the Washing Machine(PART II)

AIC model selection table and associated coefficients for hermit warbler 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt

GPUVerify: A Verifier for GPU Kernels

We present a technique for verifying race- and divergence-freedom of GPU kernels that are written in mainstream ker-nel programming languages such as OpenCL and CUDA. Our approach is founded on a novel formal operational se-mantics for GPU programming termed synchronous, delayed visibility (SDV) semantics. The SDV semantics provides a precise definition of barrier divergence in GPU kernels and allows kernel verification to be reduced to analysis of a sequential program, thereby completely avoiding the need to reason about thread interleavings, and allowing existing modular techniques for program verification to be leveraged. We describe an efficient encoding for data race detection and propose a method for automatically inferring loop invari-ants required for verification. We have implemented these techniques as a practical verification tool, GPUVerify, which can be applied directly to OpenCL and CUDA source code. We evaluate GPUVerify with respect to a set of 163 kernels drawn from public and commercial sources. Our evaluation demonstrates that GPUVerify is capable of efficient, auto-matic verification of a large number of real-world kernels

Forest degradation drives widespread avian habitat and population declines

In many regions of the world, forest management has reduced old forest and simplified forest structure and composition. We hypothesized that such forest degradation has resulted in long-term habitat loss for forest-associated bird species of eastern Canada (130,017 km2) which, in turn, has caused bird-population declines. Despite little change in overall forest cover, we found substantial reductions in old forest as a result of frequent clear-cutting and a broad-scale transformation to intensified forestry. Back-cast species distribution models revealed that breeding habitat loss occurred for 66% of the 54 most common species from 1985 to 2020 and was strongly associated with reduction in old age classes. Using a long-term, independent dataset, we found that habitat amount predicted population size for 94% of species, and habitat loss was associated with population declines for old-forest species. Forest degradation may therefore be a primary cause of biodiversity decline in managed forest landscapes

A structural basis for prion strain diversity

Recent cryogenic electron microscopy (cryo-EM) studies of infectious, ex vivo, prion fibrils from hamster 263K and mouse RML prion strains revealed a similar, parallel in-register intermolecular β-sheet (PIRIBS) amyloid architecture. Rungs of the fibrils are composed of individual prion protein (PrP) monomers that fold to create distinct N-terminal and C-terminal lobes. However, disparity in the hamster/mouse PrP sequence precludes understanding of how divergent prion strains emerge from an identical PrP substrate. In this study, we determined the near-atomic resolution cryo-EM structure of infectious, ex vivo mouse prion fibrils from the ME7 prion strain and compared this with the RML fibril structure. This structural comparison of two biologically distinct mouse-adapted prion strains suggests defined folding subdomains of PrP rungs and the way in which they are interrelated, providing a structural definition of intra-species prion strain-specific conformations

第829回千葉医学会例会・第8回千葉精神科集談会

AIC model selection table and associated coefficients for Hammond's flycatcher 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt