Tejaswi Agarwal

Bio: Tejaswi Agarwal is an academic researcher from VIT University. The author has contributed to research in topics: CUDA & Image resolution. The author has an hindex of 3, co-authored 11 publications receiving 25 citations. Previous affiliations of Tejaswi Agarwal include University of Missouri.

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

Abstract: RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time. In this paper, we present a GPU based RANSAC algorithm with pre-processing of the assumed sample set of hypothetical inliers by Monte Carlo method. Based on our implementation and results using the Point Cloud Library and NVIDIA CUDA framework for data intensive tasks we obtain significant improvement in the performance of plane segmentation algorithm over the randomly sampled subset of hypothetical inliers. The final consensus set is formed with less number of iterations using our pre-processing model. We can conclude that a pre-processed sample set of hypothetical inliers results in a faster determination of the consensus set consisting of maximum inliers.

Design of a Hybrid MPI-CUDA Benchmark Suite for CPU-GPU Clusters

Abstract: In the last few years, GPUs have become an integral part of HPC clusters. To test these heterogeneous CPU-GPU systems, we designed a hybrid CUDA-MPI benchmark suite that consists of three communication- and compute-intensive applications: Matrix Multiplication (MM), Needleman-Wunsch (NW) and the ADFA compression algorithm [1]. The main goal of this work is to characterize these workloads on CPU-GPU clusters. Our benchmark applications are designed to allow cluster administrators to identify bottlenecks in the cluster, to decide if scaling applications to multiple nodes would improve or decrease overall throughput and to design effective scheduling policies. Our experiments show that inter-node communication can significantly degrade the throughput of communication-intensive applications. We conclude that the scalability of the applications depends primarily on two factors: the cluster configuration and the applications characteristics.

Design and Implementation of an IP based authentication mechanism for Open Source Proxy Servers in Interception Mode

Abstract: Proxy servers are being increasingly deployed at organizations for performance benefits; however, there still exists drawbacks in ease of client authentication in interception proxy mode mainly for Open Source Proxy Servers. Technically, an interception mode is not designed for client authentication, but implementation in certain organizations does require this feature. In this paper, we focus on the World Wide Web, highlight the existing transparent proxy authentication mechanisms, its drawbacks and propose an authentication scheme for transparent proxy users by using external scripts based on the clients Internet Protocol Address. This authentication mechanism has been implemented and verified on Squid-one of the most widely used HTTP Open Source Proxy Server.

A flexible scheduling framework for heterogeneous CPU-GPU clusters

Abstract: In the last few years, thanks to their computational power and progressively increased programmability, GPUs have become part of HPC clusters. As a result, widely used open-source cluster resource managers (e.g. TORQUE and SLURM) have recently been extended with GPU support capabilities. These systems, however, treat GPUs as dedicated resources and provide scheduling mechanisms that often result in resource underutilization and, thereby, in suboptimal performance. We propose a cluster-level scheduler and integrate it with our previously proposed node-level GPU virtualization runtime [1, 2], thus providing a hierarchical cluster resource management framework that allows the efficient use of heterogeneous CPU-GPU clusters. The scheduling policy used by our system is configurable, and our scheduler provides administrators with a high-level API that allows easily defining custom scheduling policies. We provide two application- and hardware-heterogeneity-aware cluster-level scheduling schemes for hybrid MPI-CUDA applications: co-location- and latency-reduction-based scheduling, and use them in combination with a preemption-based GPU sharing policy implemented at the node-level. We validate our framework on two heterogeneous clusters: one consisting of commodity workstations and the other of high-end nodes with various hardware configurations, and on a mix of communication- and compute-intensive applications. Our experiments show that, by better utilizing the available resources, our scheduling framework outperforms existing batch-schedulers both in terms of throughput and application latency.

Design and Implementation of an IP based authentication mechanism for Open Source Proxy Servers in Interception Mode

Abstract: Proxy servers are being increasingly deployed at organizations for performance benefits; however, there still exists drawbacks in ease of client authentication in interception proxy mode mainly for Open Source Proxy Servers. Technically, an interception mode is not designed for client authentication, but implementation in certain organizations does require this feature. In this paper, we focus on the World Wide Web, highlight the existing transparent proxy authentication mechanisms, its drawbacks and propose an authentication scheme for transparent proxy users by using external scripts based on the clients Internet Protocol Address. This authentication mechanism has been implemented and verified on Squid-one of the most widely used HTTP Open Source Proxy Server.

SC-RANSAC: Spatial consistency on RANSAC

Abstract: The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC's runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.

Determination of Operating Parameters and Performance Analysis of Computer Networks with Paraconsistent Annotated Evidential Logic Eτ

Abstract: Computer networks have two important characteristics: the vast diversity of connecting devices and a great variability of the physical distribution of equipments. Therefore, the performance analysis of a specific network based on absolute references or third parties may not be applicable in all circumstances, especially in highly complex and heterogeneous networks. Indeed, it carries a high degree of uncertainty, and the classical logic may not be appropriate to deal it. This paper aims to parameterize and evaluate the operating elements of heterogeneous networks, from the analysis of representative attributes, based on concepts of Paraconsistent Annotated Evidential Logic Eτ.

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

Abstract: We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera.

Viewer optimized model compression

Abstract: Technologies may be generally described to provide viewer optimized compression of a model. In some examples, a computing device may receive a request to compress a master model for a viewer. The computing device may determine shape primitives of the master model through use of a shape primitive identification technique such as a random sample consensus (RANSAC) technique. The identified or determined shape primitives may be subtracted from the master model to determine residues of the master model. A processed model may be generated from the residues of the master model and the shape primitives. Visible subsets, visible based on a view cone of the viewer, of the residues and the shape primitives may be selected from the processed model, from which a compressed model may be generated. The processed model may then be used to generate a second view without redetermining the shape primitives.