Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Recent trends in software and hardware for GPGPU computing: A comprehensive survey(2010) Bayyapu, B.; Raghavendra, P.S.With the growth of Graphics Processor (GPU) programmability and processing power, graphics hardware has become a compelling platform for computationally demanding tasks in a wide variety of application domains. This state of art paper gives the technical motivations that underlie GPU computing and describe the hardware and software developments that have led to the recent interest in this field. ©2010 IEEE.Item A study of performance scalability by parallelizing loop iterations on multi-core SMPs(2010) Raghavendra, P.S.; Behki, A.K.; Hariprasad, K.; Mohan, M.; Jain, P.; Bhat, S.S.; Thejus, V.M.; Prabhu, V.Today, the challenge is to exploit the parallelism available in the way of multi-core architectures by the software. This could be done by re-writing the application, by exploiting the hardware capabilities or expect the compiler/software runtime tools to do the job for us. With the advent of multi-core architectures ([1] [2]), this problem is becoming more and more relevant. Even today, there are not many run-time tools to analyze the behavioral pattern of such performance critical applications, and to re-compile them. So, techniques like OpenMP for shared memory programs are still useful in exploiting parallelism in the machine. This work tries to study if the loop parallelization (both with and without applying transformations) can be a good case for running scientific programs efficiently on such multi-core architectures. We have found the results to be encouraging and we strongly feel that this could lead to some good results if implemented fully in a production compiler for multi-core architectures. © Springer-Verlag Berlin Heidelberg 2010.Item Comparative study of neural networks and K-means classification in web usage mining(2010) Raghavendra, P.S.; Chowdhury, S.R.; Kameswari, S.V.There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests. In this paper, we model user behaviour as a vector of the time he spends at each URL, and further classify a new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, artificial neural networks, and artificial neural networks with standardised inputs were implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour.Item Alignment based similarity distance measure for better web sessions clustering(Elsevier B.V., 2011) Poornalatha, G.; Raghavendra, P.S.The evolution of the internet along with the popularity of the web has attracted a great attention among the researchers to web usage mining. Given that, there is an exponential growth in terms of amount of data available in the web that may not give the required information immediately; web usage mining extracts the useful information from the huge amount of data available in the web logs that contain information regarding web pages accessed. Due to this huge amount of data, it is better to handle small group of data at a time, instead of dealing with entire data together. In order to cluster the data, similarity measure is essential to obtain the distance between any two user sessions. The objective of this paper is to propose a technique, to measure the similarity between any two user sessions based on sequence alignment technique that uses the dynamic programming method. © 2011 Published by Elsevier Ltd.Item Web user session clustering using modified K-means algorithm(2011) Poornalatha, G.; Raghavendra, P.S.The proliferation of internet along with the attractiveness of the web in recent years has made web mining as the research area of great magnitude. Web mining essentially has many advantages which makes this technology attractive to researchers. The analysis of web user's navigational pattern within a web site can provide useful information for applications like, server performance enhancements, restructuring a web site, direct marketing in e-commerce etc. The navigation paths may be explored based on some similarity criteria, in order to get the useful inference about the usage of web. The objective of this paper is to propose an effective clustering technique to group users' sessions by modifying K-means algorithm and suggest a method to compute the distance between sessions based on similarity of their web access path, which takes care of the issue of the user sessions that are of variable length. © 2011 Springer-Verlag.Item Achieving operational efficiency with cloud based services(2011) Bellur, K.V.; Krupal, M.; Jain, P.; Raghavendra, P.S.Cloud Computing is the evolution of a variety of technologies that have come together to alter an organization's approach to building IT infrastructure. It borrows from several computing techniques - grid computing, cluster computing, software-as-a-service, utility computing, autonomic computing and many more. It provides a whole new deployment model for enterprise web-applications. The cloud proposes significant cost cuts when compared to using an internal IT infrastructure. The "pay for what you use" model of cloud computing is significantly cheaper for a company than the "pay for everything up front" model of internal IT. Hardware Virtualization is the enabling technology behind many of the cloud infrastructure vendor offerings. Through virtualization, a physical server can be partitioned into any number of virtual servers running their own operating systems, in their allocated memory, CPU and disk footprints. From the perspective of the user or application on the virtual server, no indication exists to suggest that the server is not a real, physical server. In this paper, we make an attempt to enhance dynamic cloud based services using efficient load balancing techniques. We describe various steps involved in developing and utilizing cloud based infrastructure in such a way that cloud based services can be offered to users in an efficient manner. In the design of load balancing algorithms for an application offering cloud based services, the various details described in this paper offer useful insight, while the actual implementation may be based on the exact requirements at hand. © 2011 IEEE.Item CSPR: Column only SPARSE matrix representation for performance improvement on GPU architecture(2011) Bayyapu, B.; Raghavendra, P.S.General purpose computation on graphics processing unit (GPU) is prominent in the high performance computing era of this time. Porting or accelerating the data parallel applications onto GPU gives the default performance improvement because of the increased computational units. Better performances can be seen if application specific fine tuning is done with respect to the architecture under consideration. One such very widely used computation intensive kernel is sparse matrix vector multiplication (SPMV) in sparse matrix based applications. Most of the existing data format representations of sparse matrix are developed with respect to the central processing unit (CPU) or multi cores. This paper gives a new format for sparse matrix representation with respect to graphics processor architecture that can give 2x to 5x performance improvement compared to CSR (compressed row format), 2x to 54x performance improvement with respect to COO (coordinate format) and 3x to 10 x improvement compared to CSR vector format for the class of application that fit for the proposed new format. It also gives 10% to 133% improvements in memory transfer (of only access information of sparse matrix) between CPU and GPU. This paper gives the details of the new format and its requirement with complete experimentation details and results of comparison. © 2011 Springer-Verlag.Item A GPU framework for sparse matrix vector multiplication(Institute of Electrical and Electronics Engineers Inc., 2014) Bayyapu, B.; Guddeti, G.R.M.; Raghavendra, P.S.The hardware and software evolutions related to Graphics Processing Units (GPUs), for general purpose computations, have changed the way the parallel programming issues are addressed. Many applications are being ported onto GPU for achieving performance gain. The GPU execution time is continuously optimized by the GPU programmers while optimizing pre-GPU computation overheads attracted the research community in the recent past. While GPU executes the programs given by a CPU, pre-GPU computation overheads does exists and should be optimized for a better usage of GPUs. The GPU framework proposed in this paper improves the overall performance of the application by optimizing pre-GPU computation overheads along with GPU execution time. This paper proposes a sparse matrix format prediction tool to predict an optimal sparse matrix format to be used for a given input matrix by analyzing the input sparse matrix and considering pre-GPU computation overheads. The sparse matrix format predicted by the proposed method is compared against the best performing sparse matrix formats posted in the literature. The proposed model is based on the static data that is available from the input directly and hence the prediction overhead is very small. Compared to GPU specific sparse format prediction, the proposed model is more inclusive and precious in terms of increasing overall application's performance. © 2014 IEEE.Item Predicting an optimal sparse matrix format for SpMV computation on GPU(IEEE Computer Society help@computer.org, 2014) Bayyapu, B.; Guddeti, G.R.M.; Raghavendra, P.S.Many-threaded architecture based Graphics Processing Units (GPUs) are good for general purpose computations for achieving high performance. The processor has latency hiding mechanism through which it hides the memory access time in such a way that when one warp (group of 32 threads) is computing, the other warps perform memory bound access. But for memory access bound irregular applications such as Sparse Matrix Vector Multiplication (SpMV), memory access times are high and hence improving the performance of such applications on GPU is a challenging research issue. Further, optimizing SpMV time on GPU is an important task for iterative applications like jacobi and conjugate gradient. However, there is a need to consider the overheads caused while computing SpMV on GPU. Transforming the input matrix to a desired format and communicating the data from CPU to GPU are non-trivial overheads associated with SpMV computation on GPU. If the chosen format is not suitable for the given input sparse matrix then desired performance improvements cannot be achieved. Motivated by this observation, this paper proposes a method to chose an optimal sparse matrix format, focusing on the applications where CPU to GPU communication time and pre-processing time are nontrivial. The experimental results show that the predicted format by the model matches with that of the actual high performing format when total SpMV time in terms of pre-processing time, CPU to GPU communication time and SpMV computation time on GPU, is taken into account. The model predicts an optimal format for any given input sparse matrix with a very small overhead of prediction within an application. Compared to the format to achieve high performance only on GPU, our approach is more comprehensive and valuable. This paper also proposes to use a communication and pre-processing overhead optimizing sparse matrix format to be used when these overheads are non trivial. © 2014 IEEE.