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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

[서평]「The Unified Modeling Language User Guide」

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

/pdf/handbook-of-software-reliability-engineering-32dwvaljmm.pdf

Handbook of software reliability engineering

コンピュータ・サイエンス : ACM computing surveys

With the growing demand for performance-oriented problems, programmers routinely execute the embarrassing parallel part of the application (GPU kernels) in a GPU in order to achieve signi?cant speedup. These applications are becoming complex and long-running which makes it energy inef?cient. Anticipating its execution time can help the developers to ?x the inef?cient code before running it. In this paper, we propose an approach to predict the execution time of a GPU kernel without the need of executing it. We build an analytical model to predict the execution time of a GPU kernel by analyzing the intermediate PTX code of a CUDA kernel. Our experimental analysis of a set of benchmarks shows that for 45 applications the estimated execution time has the mean absolute error of 26.86% when compared to the actual execution time. Mean absolute error for benchmarks belonging to Dynamic programming dwarf is minimum, followed by Dense Linear Algebra benchmarks.

Predicting Execution Time of CUDA Kernel Using Static Analysis

A good design abstraction framework for high performance computing should provide a higher level programming abstraction that strikes a balance between the abstraction and visibility over the hardware so that the software developer can write a portable software without having to understand the hardware nuances, yet exploit the compute power optimally. In this paper we have analyzed a popular design abstraction framework called "Thrust" from NVIDIA, and proposed an extension called Thrust++ that provides abstraction over the memory hierarchy of an NVIDIA GPU. Thrust++ allows developers to make efficient use of shared memory and overall, provides better control over the GPU memory hierarchy while writing applications in Thrust style for the CUDA backend. We have shown that when applications are written for the CUDA backend using Thrust++, they have minimal performance degradation when compared to their equivalent CUDA versions. Further, Thrust++ provides almost 4x speedup when compared to Thrust, for certain compute intensive kernels that repeatedly use the reduce operation.

Thrust++: Extending Thrust Framework for Better Abstraction and Performance

Life science industry is looking towards new and cost-effective ways to manage and analyze huge amount of genomic data for faster innovation in drug or biologics discovery. To that effect, various alliances among competitive organizations are getting formed, such as the Pistoia Alliance, to collaborate and share a pool of genomic data and build useful search and analysis techniques for the alliance partners. In order to make the development, and management of data and applications cost-effective, a secure cloud computing based platforms are being considered. In this paper we describe an experience report of building such a collaborative platform on Amazon cloud platform. In order to build a scalable genome sequence alignment solution, we have adopted the well-known BLAST framework on Hadoop platform. A major challenge here is that the BLAST executable requires to be ported as it is, and yet the execution needs to scale, as the number of jobs increases, by elastically growing the Hadoop infrastructure. In this paper we proposed a BLAST database partitioning solution to achieve optimal scalability. Our controlled experiment is encouraging, the empirical result shows that the job execution scales with the number of jobs, if the partition sizes are chosen appropriately.

Implementation of a Scalable Next Generation Sequencing Business Cloud Platform--An Experience Report

Likely system invariants model properties that hold in operating conditions of a computing system. Invariants may be mined offline from training datasets, or inferred during execution. Scientific work has shown that invariants’ mining techniques support several activities, including capacity planning and detection of failures, anomalies and violations of Service Level Agreements. However their practical application by operation engineers is still a challenge. We aim to fill this gap through an empirical analysis of three major techniques for mining invariants in cloud-based utility computing systems: clustering, association rules, and decision list. The experiments use independent datasets from real-world systems: a Google cluster, whose traces are publicly available, and a Software-as-a-Service platform used by various companies worldwide. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. A sensitivity analysis is performed. Experimental results allow inferring practical usage implications, showing that relatively few invariants characterize the majority of operating conditions, that precision and recall may drop significantly when trying to achieve a large coverage, and that techniques exhibit similar precision, though the supervised one a higher recall. Finally, we propose a general heuristic for selecting likely invariants from a dataset.

Assessing Invariant Mining Techniques for Cloud-Based Utility Computing Systems

The hybrid cloud idea is increasingly gaining momentum because it brings distinct advantages as a hosting platform for complex software systems. However, there are several challenges that need to be surmounted before hybrid hosting can become pervasive and penetrative. One main problem is to architecturally partition workloads across permutations of feasible cloud and non‐cloud deployment choices to yield the best‐fit hosting combination. Another is to predict the effort estimate to deliver such an advantageous hybrid deployment. In this paper, we describe a heuristic solution to address the said obstacles and converge on the ideal hybrid cloud deployment architecture, based on properties and characteristics of workloads that are sought to be hosted. We next propose a model to represent such a hybrid cloud deployment and demonstrate a method to estimate the effort required to implement and sustain that deployment. We also validate our model through dozens of case studies spanning several industry verticals and record results pertaining to how the industrial grouping of a software system can impact the aforementioned hybrid deployment model. Copyright © 2017 John Wiley & Sons, Ltd.

Santonu Sarkar

Papers

Predicting Execution Time of CUDA Kernel Using Static Analysis

Thrust++: Extending Thrust Framework for Better Abstraction and Performance

Implementation of a Scalable Next Generation Sequencing Business Cloud Platform--An Experience Report

Assessing Invariant Mining Techniques for Cloud-Based Utility Computing Systems

Architectural partitioning and deployment modeling on hybrid clouds