The growing complexity and scale of Internet systems coupled with the improved capabilities of Machine Learning (ML) methods in recent years have motivated researchers and practitioners to increasingly rely on these methods for data-driven design and analysis of wide range of problems in network systems such as detecting network attacks, performing resource management, or improving quality of service (QoS).
We explore the application of highly expressive logical and automated reasoning techniques to the analysis of computer programs. We begin with an introduction to formal methods by describing different approaches and the strength of the properties they can guarantee. These range from static analyzers, SMT solvers, deductive program provers, and proof assistants. We then explore applications of formal methods to the analysis of intermediate representations, verification of floating point arithmetic, and fine-grained parallelism such as vectorization.
Driven by massive dataset corpuses and advances and programmability in accelerator architectures, such as GPUs and FPGAs, machine learning (ML) has delivered remarkable, human-like accuracy in tasks such as image recognition, machine translation and speech processing. Although ML has improved accuracy in selected human tasks, the time to train models can range from hours to weeks. Thus, accelerating model training is an important research challenge facing the ML field.
The performance model of an application can provide insight about its runtime behavior on particular hardware. Such information can be analyzed by both developers and researchers for performance tuning. In this talk, we explore different performance modeling techniques and categorize them into three groups: Analytical modeling; empirical modeling and simulation-based modeling.
This study surveys the state-of-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures.
Complexity theory has attempted to explain why some computational tasks are harder to solve than others. This involves providing a measurement of the computational resources required to solve problems and grouping problems together that have similar resource requirements.
This paper explores the use of process automation to guide a parallel performance analyst through the knowledge discovery process, while providing the ability to customize the analysis process. For example, the input data can be evaluated to determine the distribution of the data, the standard deviation and/or the prevalence of outliers. Different analytical methods assume or require particular distributions or other data characteristics, and process automation would help prevent the misapplication of inappropriate analytical methods.
Personalization has been applied and researched in the field of adaptive user interface, e-commerce, and requirements engineering. Many personalized systems don?t work well because of difficulty in inferring users? characteristics, particularly in the early stage of application usage. I propose that the alternative is to pay attention to users? abilities, goals, and preferences at the very early stage of requirements engineering. I will use the Personal and Contextual Requirements Engineering Framework to approach personalization.
Evolution advances the fitness of an organism through a series of small, incremental changes over long periods of time. In contrast to this slow, stochastic process, the theory of whole genome duplication proposes a means for rapid evolutionary change and diversification. This theory, first proposed in the early 1970's, was considered controversial until recently.
Since the early stages of human language, puns have been an important component of it. The proof for this is the fact that Sumerian cuneiform and Egyptian hieroglyphs, which are two of the earliest human writing systems were based on punning systems. In contemporaneity applications, puns are abundant in literature, advertising and social media. However, despite the importance of puns in human language understanding and its long history, computational detection of puns is scarce.