With the software applications increasing in complexity, description of hardware is becoming increasingly relevant. To ensure the quality of service for specific applications, it is imperative to have an insight into hardware resources. Cache memory is used for storing data closer to the processor needed for quick access and improves the quality of service of applications. The description of cache memory usually consists of the size of different cache levels, set associativity, or line size. Software applications would benefit more from a more detailed model of cache memory.In this thesis, we offer a way of describing the behavior of cache memory which benefits software performance. Several performance events are tested, including L1 cache misses, L2 cache misses, and L3 cache misses. With the collected information, we develop performance models of cache memory behavior. Goodness of fit is tested for these models and they are used to predict the behavior of the cache memory during future runs of the same application.Our experiments show that L1 cache misses can be modeled to predict the future runs. L2 cache misses model is less accurate but still usable for predictions, and L3 cache misses model is the least accurate and is not feasible to predict the behavior of the future runs.

Researchers have made significant progress in information extraction from short documents in the last few years, including social media interaction, news articles, and email excerpts. This research aims to extract technical entities like hardware resources, computing platforms, compute time, programming language, and libraries from scholarly research articles. Research articles are generally long documents having both salient as well as non-salient entities. Analyzing the cross-sectional relation, filtering the relevant information, measuring the saliency of mentioned entities, and extracting novel entities are some of the technical challenges involved in this research. This work presents a detailed study about the performance, effectiveness, and scalability of rule-based weakly supervised algorithms. We also develop an automated end-to-end Research Entity and Relationship Extractor (E2R Extractor). Additionally, we perform a comprehensive study about the effectiveness of existing deep learning-based information extraction tools like Dygie, Dygie++, SciREX. The research also contributes a dataset containing novel entities annotated in BILUO format and represents the baseline results using the E2R extractor on the proposed dataset. The results indicate that the E2R extractor successfully extracts salient entities from research articles. / Master of Science / Information extraction is a process of automatically extracting meaningful information from unstructured text such as articles, news feeds and presenting it in a structured format. Researchers have made significant progress in this domain over the past few years. However, their work primarily focuses on short documents such as social media interactions, news articles, email excerpts, and not on long documents such as scholarly articles and research papers. Long documents contain a lot of redundant data, so filtering and extracting meaningful information is quite challenging. This work focuses on extracting entities such as hardware resources, compute platforms, and programming languages used in scholarly articles. We present a deep learning-based model to extract such entities from research articles and research papers. We evaluate the performance of our deep learning model against simple rule-based algorithms and other state-of-the-art models for extracting the desired entities. Our work also contributes a labeled dataset containing the entities mentioned above and results obtained on this dataset using our deep learning model.