Performance Modeling of Parallel Applications on MPSoCs

In this paper we present a new technique for automatically measuring the performance of tasks, functions or arbitrary parts of a program on a multiprocessor embedded system. The technique instruments the tasks described by OpenMP, used to represent the task parallelism, while ad hoc pragmas in the source indicate other pieces of code to profile. The annotations and the instrumentation are completely target-independent, so the same code can be measured on different target architectures, on simulators or on prototypes. We validate the approach on a single and on a dual LEON 3 platform synthesized on FPGA, demonstrating a low instrumentation overhead. We show how the information obtained with this technique can be easily exploited in a hardware/software design space exploration tool, by estimating, with good accuracy, the speed-up of a parallel application given the profiling on the single processor prototype

Performance Estimation for Task Graphs Combining Sequential Path Profiling and Control Dependence Regions

The speed-up estimation of parallelized code is crucial to efficiently compare different parallelization techniques or task graph transformations. Unfortunately, most of the time, during the parallelization of a specification, the information that can be extracted by profiling the corresponding sequential code (e.g. the most executed paths) are not properly taken into account. In particular, correlating sequential path profiling with the corresponding parallelized code can help in the identification of code hot spots, opening new possibilities for automatic parallelization. For this reason, starting from a well-known profiling technique, the Efficient Path Profiling, we propose a methodology that estimates the speed-up of a parallelized specification, just using the corresponding hierarchical task graph representation and the information coming from the dynamic profiling of the initial sequential specification. Experimental results show that the proposed solution outperforms existing approaches

Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

A Quantum Planner for Robot Motion

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot's behavior. According to the production rules, the planning of the robot activities is processed in a recognize-act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up

Automatic Creation of High-Bandwidth Memory Architectures from Domain-Specific Languages: The Case of Computational Fluid Dynamics

Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism. Modern FPGA devices can leverage high-bandwidth memory technologies, but when applications are memory-bound designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. This development process requires hardware design skills that are uncommon in domain-specific experts. In this paper, we propose an automated tool flow from a domain-specific language (DSL) for tensor expressions to generate massively-parallel accelerators on HBM-equipped FPGAs. Designers can use this flow to integrate and evaluate various compiler or hardware optimizations. We use computational fluid dynamics (CFD) as a paradigmatic example. Our flow starts from the high-level specification of tensor operations and combines an MLIR-based compiler with an in-house hardware generation flow to generate systems with parallel accelerators and a specialized memory architecture that moves data efficiently, aiming at fully exploiting the available CPU-FPGA bandwidth. We simulated applications with millions of elements, achieving up to 103 GFLOPS with one compute unit and custom precision when targeting a Xilinx Alveo U280. Our FPGA implementation is up to 25x more energy efficient than expert-crafted Intel CPU implementations

Results on Multiple Coulomb Scattering from 12 and 20 GeV electrons on Carbon targets

Multiple scattering effects of 12 and 20 GeV electrons on 8 and 20 mm thickness carbon targets have been studied with high-resolution silicon microstrip detectors of the UA9 apparatus at the H8 line at CERN. Comparison of the scattering angle between data and GEANT4 simulation shows excellent agreement in the core of the distributions leaving some residual disagreement in the tails.Comment: 14 pages, 16 figures. Updated to match published versio

Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach

Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular failure (N = 8, 11%) or chronic–right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an “all-subsets” approach. Results: Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular–free wall were the most significant predictors of acute–right ventricular failure (maximum receiver operating characteristic–area under the curve = 0.95, 95% confidence interval = 0.91–1.00, by the naïve Bayes), while the right ventricular–free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic–area under the curve = 0.97, 95% confidence interval = 0.91–1.00, according to naïve Bayes). Conclusion: Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute–right ventricular failure and chronic–right ventricular failure, respectively

Calcium as a key player in arrhythmogenic cardiomiopathy : adhesion disorder or intracellular alteration?

Arrhythmogenic cardiomyopathy (ACM) is an inherited heart disease characterized by sudden death in young people and featured by fibro-adipose myocardium replacement, malignant arrhythmias, and heart failure. To date, no etiological therapies are available. Mutations in desmosomal genes cause abnormal mechanical coupling, trigger pro-apoptotic signaling pathways, and induce fibro-adipose replacement. Here, we discuss the hypothesis that the ACM causative mechanism involves a defect in the expression and/or activity of the cardiac Ca2+ handling machinery, focusing on the available data supporting this hypothesis. The Ca2+ toolkit is heavily remodeled in cardiomyocytes derived from a mouse model of ACM defective of the desmosomal protein plakophilin-2. Furthermore, ACM-related mutations were found in genes encoding for proteins involved in excitation\u2012contraction coupling, e.g., type 2 ryanodine receptor and phospholamban. As a consequence, the sarcoplasmic reticulum becomes more eager to release Ca2+, thereby inducing delayed afterdepolarizations and impairing cardiac contractility. These data are supported by preliminary observations from patient induced pluripotent stem-cell-derived cardiomyocytes. Assessing the involvement of Ca2+ signaling in the pathogenesis of ACM could be beneficial in the treatment of this life-threatening disease

Regional biomechanical characterization of human ascending aortic aneurysms: Microstructure and biaxial mechanical response

: The ascending thoracic aortic aneurysm (ATAA) is a permanent dilatation of the vessel with a high risk of adverse events, and shows heterogeneous properties. To investigate regional differences in the biomechanical properties of ATAAs, tissue samples were collected from 10 patients with tricuspid aortic valve phenotype and specimens from minor, anterior, major, and posterior regions were subjected to multi-ratio planar biaxial extension tests and second-harmonic generation (SHG) imaging. Using the data, parameters of a microstructure-motivated constitutive model were obtained considering fiber dispersion. SHG imaging showed disruptions in the organization of the layers. Structural and material parameters did not differ significantly between regions. The non-symmetric fiber dispersion model proposed by Holzapfel et al. [25] was used to fit the data. The mean angle of collagen fibers was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. The experimental data collected in this study contribute to the biomechanical data available in the literature on human ATAAs. Region-specific parameters for the constitutive models are fundamental to improve the current risk stratification strategies, which are mainly based on aortic size. Such investigations can facilitate the development of more advanced finite element models capable of capturing the regional heterogeneity of pathological tissues. STATEMENT OF SIGNIFICANCE: Tissue samples of human ascending thoracic aortic aneurysms (ATAA) were collected. Samples from four regions underwent multi-ratio planar biaxial extension tests and second-harmonic generation imaging. Region-specific parameters of a microstructure-motivated model considering fiber dispersion were obtained. Structural and material parameters did not differ significantly between regions, however, the mean fiber angle was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. This study provides a unique set of mechanical and structural data, supporting the microstructural influence on the tissue response. It may facilitate the development of better finite element models capable of capturing the regional tissue heterogeneity

Recurrent Subarachnoid Bleeding and Superficial Siderosis in a Patient with Histopathologically Proven Cerebral Amyloid Angiopathy

A 68-year-old man with a history of hypertension presented with recurrent subarachnoid bleeding. Brain MRI showed superficial siderosis, and diagnostic cerebral angiograms did not show any intracranial vascular malformation or arterial aneurism. Post mortem neuropathological examination of the brain was consistent with a diagnosis of cerebral amyloid angiopathy. Clinicians should be aware that cerebral amyloid angiopathy should be considered in patients with unexplained recurrent subarachnoid bleeding, even in cases without familial clustering or transthyretin variant