A co-located partitions strategy for parallel CFD–DEM couplings

The pattern design of superhydrophobic surfaces can be significantly aided by computations that predict the Cassie-Baxter (CB) to Wenzel (W) transition, which is responsible for the break-down of superhydrophobic behavior. We present a computational framework for the optimization of patterned surfaces based on the energy barriers of the CB-W transitions which comprises the following elements: (a) design of structured surface patterns, for example, arrays of pillars, with parameterized geometric features such as size, pitch, slope, and roundness. (b) Computation of the wetting states with a modified Young-Laplace equation that facilitates the introduction of solid/liquid interactions for complex surface patterns and has significantly lower computational cost than other commonly used methods, such as the volume-of-fluid, phase-field, and so forth. (c) Incorporation of the modified Young-Laplace in the simplified string method, allowing the calculation of the minimum energy paths of wetting transitions which, apart from the energy barriers, also reveal the transition mechanisms (CB failure modes). (d) Accommodation of large-scale problems with good parallel performance and scalability on multicore-distributed memory systems using fast iterative solvers and the Message Passing Interface communication protocol. We demonstrate the computational framework with a shape optimization study of inverted conical frustum pillars. The optimization objective function is the resistance to the CB-W transition, which is quantified by the energy barrier-a relatively large energy barrier suggests improved superhydrophobicity. We also report the parallel performance, in terms of parallel speedup for problems ranging from three hundred thousands to 12 million degrees of freedom, solved using up to 40 processing cores.

H.26L is an emerging video encoding standard proposed by ITU, which goal is for video communication and multicast in low bit rate conditions. We give detailed analysis and performance tests for the parallelism of the H.26L video encoder test model long-term (TML8.0). Based on these tests, several parallel schedule strategies are discussed in different aspects, and the optimal one is selected and implemented by using MPI. At last, experiment results on Dawning-2000 MPP machines with different processors are given. The analysis and simulation results show that the H.26L has a good parallel performance and scalability on slice layer, and parallel computing can greatly speed up the processing of H.26L encoder. The experiment methods and results can be applied to many parallel systems for H.26L video processing.

Partitioning strategies for parallel KIVA-4 engine simulations

We present time-parallel algorithms for parallel simulation of multiple access protocols for medium access-in particular slotted Aloha and slotted p-persistent CSMA. Two mechanisms are presented-regeneration point-based and fix up-based. Aloha is simulated using both mechanisms and CSMA is simulated using only the first mechanism. An analytical technique is developed to predict speedup for the regeneration point-based scheme for slotted Aloha. Speedup values obtained from the analytical technique are found to be in good agreement with those obtained from simulations. In general, it is observed that any mechanism that reduces the number of backlogged packets has good parallel performance regardless of the protocol simulated or the mechanism used to parallelize the simulation.

The Lighthill acoustic analogy in combination with two different higher-order CFD solvers is used to investigate the sound generation of two test cases. The flow around a cylinder at Re = 150 is analysed with a Discontinous Galerkin method and a counter-rotating open rotor (CROR) with a WENO scheme. The simulation of the cylinder is able to predict both aerodynamic and acoustic behaviour correctly, the vortex street behind the cylinder is responsible for the noise radiation similar to of an acoustic dipole. The analysis of the CROR focuses on the effect of using a higher-order method with a detailed comparison with a standard second order method. While global aerodynamic forces show only small differences, the better transport of vortices, especially of the blade tip vortex, is a benefit for the prediction of interaction noise of the two rotors. This paper includes different investigations on the new HLRS Cray Hermit cluster. The DG code was optimized for single-core usage while still maintaining its good parallel performance. The effect of node-pinning is studied with the CROR configuration which improved the computational time slightly.

The support of range, segment and rectangle queries are fundamental problems in computational geometry, and have extensive applications in many domains. Despite significant theoretical work on these problems, efficient implementations can be complicated, and most implementations do not have useful theoretical bounds. In this paper, we focus on simple and efficient parallel algorithms and implementations for range, segment and rectangle queries, which have worst-case bounds in theory and good performance in practice, both sequentially and in parallel. We propose to use a framework based on the abstract data type augmented map, to model the problems. Based on the augmented map interface, we develop both multi-level tree structures and sweepline algorithms supporting range, segment and rectangle queries in two dimensions. For the sweepline algorithms, we also propose a parallel paradigm and show corresponding cost bounds. Theoretically, the construction algorithms of all of our data structures are work-efficient and highly parallelized.We have implemented all the data structures described in the paper, ten in all, using a parallel augmented map library. Based on the library, each data structure only requires about 100 lines of C++ code. We test their performance on large data sets (up to 108 elements) and a machine with 72-cores (144 hyperthreads). The parallel construction achieves 32-68x speedup, and the speedup numbers for queries are up to 126-fold. Sequentially, each of our implementations outperforms the CGAL library by at least 2x in both construction and queries. Our sequential implementation has approximately the same construction time as the R-tree in the Boost library, but has significantly better query performance (1.6-1200x). We believe this paper provides the most comprehensive experimental study of data structures on range, segment and rectangle queries, both in parallel and sequential setting.

We have discussed the parallelisation of a 2D ADI solver for the Shallow Water Equations. The tridiagonal systems are solved using the Thomas algorithm with data transposition, the implementation of which is rather straightforward. For the tidal simulation in the Northwest European Continental Shelf Sea, a good parallel performance is achieved. We have also reported on experiments with the FORGE 90 parallelisation tool. Applications with irregular data such as the Continental Shelf Sea application, can hardly be parallelised well by using FORGE 90, mainly due to the irregularity of the data which can cause a large load imbalance. Nevertheless, the use of parallel software tools such as FORGE 90 can help to simplify the implementation work.

Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet-of-Things (IoT) technology. However, these devices primarily run C/C \({+}{+}\) code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control-flow integrity (CFI), dataflow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when it happens. We have implemented our idea on an ARM Cortex-M-based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

The World Wide Web contributes greatly to the creation of an ever-increasing global information database. One of the important experience factors while accessing it lies with the time taken to load the webpages. The latency of the network and server's response affect the page-load performance. Caching and prefetching are well-known strategies for reducing the access latency and have become essential elements of research so as to provide the users with a faster browsing experience. In this paper we propose a method of predicting the client's forthcoming requests and assisting the client in faster page-load by selectively ‘hinting’ and ‘pushing’ the predicted resources to the client. We make use of the SPDY based forward proxy for communicating with the Browser and introduce ‘SPDY session’ prefetch, We also analyze the reduction in memory consumption because of our prediction procedure.

A sparse mesh for Compact Finite Difference—Fourier solvers with radius-dependent spectral resolution in circular domains

Recently, an integral equation discontinuous Galerkin method (IEDG) has been proposed to solve electromagnetic problems. One of the most attractive features of IEDG is the possibility to employ non-conformal mesh for the implementation of the combined field integral equation (CFIE) since the square integrable L2 basis is adopted and no continuity requirement is necessary across boundaries of adjacent elements. Moreover, IEDG provides great flexibility in terms of basis function such that different orders of basis functions with different element types can be mixed with the same discretization to best represent the currents locally. This feature also facilitates the incorporation of adaption techniques to IEDG, either through the use of higher order basis functions (p refinement) or subdivide the elements into multiple smaller pieces (h refinement). All these merits makes IEDG a very attractive candidate for the solution of multi-scale and wide-band problems such that geometry components that are smooth can be discretized using coarser elements while finer discretization can be adopted for more intricate structures. For wide-band simulations, IEDG allows one to adopt the discretization that is generated at a lower frequency for the calculation at higher frequencies through adaptively refining the elements. This helps to reduce the computational burden. However, adoption of higher frequency mesh at lower frequencies could result in the well known "sub-wavelength breakdown" problem for conventional MLFMM. The computational complexity deteriorates significantly for both multi-scale or low frequency problems. In this paper, IEDG is accelerated by hierarchical multi-level fast multipole method, especially for the problems that involve the dense mesh in a local or global scale. H-MLFMM separates the evanescent physics from radiation physics and employ different transformation of basis accordingly. For the evanescent regime, where densely discretized elements reside, it identifies a reduced set of numerically independent basis functions from the original ones via a skeletonalization algorithm, which in essence is a kernel independent algebraic data spacification algorithm. The reduced set of basis functions are named "skeletons" or "skeleton DoFs" and they are adopted to represent the interaction related with the original set of basis functions. Subsequently, significant reduction in memory consumption and CPU time per matrix-vector multiplication (MVP) are achieved through the compressed coupling matrices. With the H-MLFMM incorporated, IEDG offers a very attractive approach to solve for the wide-band EM response from a target via the mesh at midfrequency.

Mining change patterns can give unique understanding on the evolution of dynamically changing systems like social relation graphs, weblinks, hardware descriptions and models. A more recent focus is source code change pattern mining that may qualitatively justify expected or uncover unexpected patterns. These patterns then offer a basis, e.g., for program language evolution or auto-completion support. We present a change pattern mining method that greatly expands the limits of input data and pattern complexity, over existing methods. We propose scalability solutions on conceptual and algorithmic level, thereby evolving the state-of-the-art sampling-based frequent subgraph mining method FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , resulting in 75% reduction in memory consumption and a speedup of 6500 for a large scale dataset. Patterns can have 100,000s of occurrences for which manual review is impossible and may lead to misinterpretation. We propose the novel content track approach for interactively exploring pattern contents in context, based on marginal distributions. We evaluate our approach by mining 1,000 open source projects contributing a total of 558 million changes and 2 billion contextual connections among them, thereby, demonstrating its scalability. A manual interpretation of 19 patterns shows sensible mined patterns allowing to deduct implications for language design and demonstrating the soundness of the approach.

An appropriate mathematical framework for perfusion imaging based on slow projection acquisition like originating from C-arm systems has already been presented in literature. Due to the huge system matrix this approach in general has an extremely high computational effort and memory consumption, thus preventing the storage in the random access memory of a computer for realistic objects and spatial resolution. To overcome this difficulty we present an efficient storage scheme for the system matrix. The main idea is to effectively utilize the underlying geometric symmetries. In this paper we investigated the performance of this the beam-form independent framework focusing on the reduction of memory consumption. The obtained results demonstrate a reduction of the overall memory requirements up to three orders of magnitude compared to savings due to the simply utilization of the matrix sparsity of the matrixes. Future work will focus on the implementation of the method for large three dimensional objects as well as using higher order symmetries.

BackgroundMining logic rules from structured knowledge bases is the basis of knowledge engineering. Due to the NP‐hardness of the rule mining problem, logic rules cannot be efficiently induced from knowledge bases, especially large‐scale ones.IdeaIn this article, we propose a compact and efficient index structure for the maintenance of the intermediate data during top‐down rule mining, such that the memory consumption can be reduced and mining efficiency can be improved.Developing PointsThe index is based on a mapping from constant symbols to integers and the sorting of the mapped integers. Index update has been dissembled into four basic operations. Moreover, the index itself acts as the cache during top‐down mining.ValueMost contributions in existing works employ algorithmic and architectural optimizations to improve efficiency. Data‐oriented optimizations have also been explored to some extent, but the data efficiency is relatively low, and the memory consumption is thus becoming a new challenge for state‐of‐the‐art systems. We tackle this challenge in this article, and our technique has been proven more efficient than state‐of‐the‐art systems. We evaluate our method on six datasets which contain up to 160 K records and are frequently used as benchmarks in tasks related to knowledge engineering. The experimental results show that the proposed technique speeds up the rule mining procedure by on average and reduces memory consumption by up to 70%. The space overhead of the data structure is about twice that of the indexed records, which is more than 80% lower than that of the state‐of‐the‐art technique.

This paper proposes an efficient patch-based image matting approach to reduce memory consumption in processing high-resolution images. Most existing image matting techniques employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory consumption. Inspired by 'divide and conquer', the image is divided into small patches self-adaptively according to the distribution of pixels, then the matting algorithm is applied in patch level. The memory consumption is related to the size of patch. Relationships between patches are also considered by locally linear embedding to maintain consistency through the whole image. Experimental results show that our method significantly reduces memory consumption while maintaining high-fidelity matting results on the benchmark dataset.