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Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow...
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small...
The conventional OpenCL 1.x style CPU-GPU heterogeneous computing paradigm treats the CPU and GPU processors as loosely connected separate entities. At best each executes independent tasks, but, more commonly, the CPU idles while waiting for results from the GPU. No data-sharing and communications are allowed during kernel execution. This model limits the number of applications that can harness the...
Modern GPUs embrace on-chip cache memory to exploit the locality present in applications. However, the behavior and effect of the cache on GPUs are different from those on conventional processors due to the Single Instruction Multiple Thread (SIMT) thread execution model and resulting memory access patterns. Previous studies report that caching data can hurt the performance due to increased memory...
In this paper, we focus on promoting multi-label learning task with ensemble learning. Compared to traditional single algorithm methods, it has been recognized that ensemble methods could achieve much better performance than each constituent learned model, especially under the conditional independence of different classifiers. Existing multi-label ensemble algorithms mainly focus on creating diverse...
Due to the simplicity of its implementation and the impressive performance, Extreme Learning Machine (ELM) has been widely used in applications of machine learning. However, there are two potential problems in ELM: 1) lack of an efficient method for minimizing error; 2) consideration of little inherent structural information about correlations among output components. To overcome those problems, this...
Many today's real-time applications, such as Advanced Driver Assistant Systems (ADAS), demand both high computing power and safety guarantees. High computing power can be easily delivered by, now ubiquitous, multi-core CPUs or by a heterogeneous system with a multi-core CPU and a parallel accelerator such as a GPU. Reaching the required safety level in such a system is by far more difficult because...
The classification performances of the traditional one-class support vector machine (OCSVM) and its variants are often not satisfying when outliers are complex. To deal with this case, assigning smaller weights to these outliers may alleviate their influence upon the classification boundary and enhance the robustness of OCSVM. In this paper, a novel adaptive-weighted one-class support vector machine...
Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world...
The power consumed by memory system in GPUs is a significant fraction of the total chip power. As thread level parallelism increases, GPUs are likely to stress cache and memory bandwidth even more, thereby exacerbating power consumption. We observe that neighboring concurrent thread arrays (CTAs) within GPU applications share considerable amount of data. However, the default GPU scheduling policy...
Hyperparameter optimization is now widely applied to tune the hyperparameters of learning algorithms. The hyperparameters can have structure, resulting in hyperparameters depending on conditions, or on the values of other hyperparameters. We target the problem of combined algorithm selection and hyperparameter optimization, which includes at least one conditional hyperparameter: the choice of the...
Big Data as expressed as "Big Graphs" are growing in importance. Looking forward, there is also increasing interest in streaming versions of the associated analytics. This paper develops an initial template for the relationship between "traditional" batch graph problems, and streaming forms. Variations of streaming problems are discussed, along with their relationship to existing...
Chapel is an emerging scalable, productive parallel programming language. In this work, we analyze Chapel's performance using The Parallel Research Kernels on two different manycore architectures including a state-of-the-art Intel Knights Landing processor. We discuss implementation techniques in Chapel and their relation to the OpenMP implementations of the PRK. We also suggest and prototype several...
The path to HPC-Big Data convergence has resulted in numerous researches that demonstrate the performance trade-off between running applications on supercomputers and cloud platforms. Previous studies typically focus either on scientific HPC benchmarks or previous cloud configurations, failing to consider all the new opportunities offered by current cloud offerings. We present a comparative study...
Recent years have seen a growing interest in neural networks whose hidden-layer weights are randomly selected, such as Extreme Learning Machines (ELMs). These models are motivated by their ease of development, high computational learning speed and relatively good results. Alternatively, constructive models that select the hidden-layer weights as a subset of the data have shown superior performance...
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using...
We present a novel strategy for automatic performance tuning of GPU computational kernels. The strategy combines heuristic search with regression trees to prune the optimization space. It samples configurations in the space and uses these samples to build a regression tree. It then focuses the search on the leaf region of the tree with the best mean sample performance. Additional configurations are...
Full-system simulators are increasingly finding their way into the consumer space for the purposes of backwards compatibility and hardware emulation (e.g. for games consoles). For such compute-intensive applications simulation performance is paramount. In this paper we argue that existing benchmark suites such as SPEC CPU2006, originally designed for architecture and compiler performance evaluation,...
It is well known that the TLB performance impacts the memory system performance, which is critical for overall system performance. Similar to multi-level caches, multilevel TLBs have become an important leverage for boosting data access performance. Applications have increasingly large working sets. Servers targeting such applications have thus been built with ever larger main memory capacities, but...
Systems with multiple forms of heterogeneity, including functional, performance, and dynamic heterogeneity, are now commercially available. The use and tuning of any of these options can impact other options and so it is important to understand their interactions. This work characterizes the power and performance implications of multiple dimensions of heterogeneity from a commercial multidimensional...
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