Chip multi-processors (CMPs) have become ubiquitous, while tools that ease concurrent programming have not. The promise of increased performance for all applications through ever more parallel hardware requires good tools for concurrent programming, especially for average programmers. Transactional memory (TM) has enjoyed recent interest as a tool that can help programmers program concurrently. The transactional memory (TM) research community is heavily invested in the claim that programming with transactional memory is easier than alternatives (like locks), but evidence for or against the veracity of this claim is scant. In this paper, we describe a user-study in which 237 undergraduate students in an operating systems course implement the same programs using coarse and fine-grain locks, monitors, and transactions. We surveyed the students after the assignment, and examined their code to determine the types and frequency of programming errors for each synchronization technique. Inexperienced programmers found baroque syntax a barrier to entry for transactional programming. On average, subjective evaluation showed that students found transactions harder to use than coarse-grain locks, but slightly easier to use than fine-grained locks. Detailed examination of synchronization errors in the students' code tells a rather different story. Overwhelmingly, the number and types of programming errors the students made was much lower for transactions than for locks. On a similar programming problem, over 70% of students made errors with fine-grained locking, while less than 10% made errors with transactions.

I would like to express my deepest gratitude to my advisor, Emmett Witchel, for help and guidance over my graduate career. There are few people who have invested more time and energy into my success than Emmett. Moreover, he has very generously supported my pursuit of independent and external projects over the last two years.There is much that is hard to summarize adequately, so I simply say thank you.

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An error occurs when software cannot complete a requested action as a result of some problem with its input, configuration, or environment. A high-quality error report allows a user to understand and correct the problem. Unfortunately, the quality of error reports has been decreasing as software becomes more complex and layered. End-users take the cryptic error messages given to them by programs and struggle to fix their problems using search engines and support websites. Developers cannot improve their error messages when they receive an ambiguous or otherwise insufficient error indicator from a black-box software component.We introduce Clarify, a system that improves error reporting by classifying application behavior. Clarify uses minimally invasive monitoring to generate a behavior profile, which is a summary of the program's execution history. A machine learning classifier uses the behavior profile to classify the application's behavior, thereby enabling a more precise error report than the output of the application itself.We evaluate a prototype Clarify system on ambiguous error messages generated by large, modern applications like gcc, LaTeX, and the Linux kernel. For a performance cost of less than 1% on user applications and 4.7% on the Linux kernel, the prototype correctly disambiguates at least 85% of application behaviors that result in ambiguous error reports. This accuracy does not degrade significantly with more behaviors: a Clarify classifier for 81 LaTeX error messages is at most 2.5% less accurate than a classifier for 27 LaTeX error messages. Finally, we show that without any human effort to build a classifier, Clarify can provide nearest-neighbor software support, where users who experience a problem are told about 5 other users who might have had the same problem. On average 2.3 of the 5 users that Clarify identifies have experienced the same problem.

Modern discrete GPUs support unified memory and demand paging. Automatic management of data movement between CPU memory and GPU memory dramatically reduces developer effort. However, when application working sets exceed physical memory capacity, the resulting data movement can cause great performance loss. This paper proposes a memory management framework, called ETC, that transparently improves GPU performance under memory oversubscription using new techniques to overlap eviction latency of GPU pages, reduce thrashing cost, and increase effective memory capacity. Eviction latency can be hidden by eagerly creating space for demand-paged data with proactive eviction (E). Thrashing costs can be ameliorated with memory-aware throttling (T), which dynamically reduces the GPU parallelism when page fault frequencies become high. Capacity compression (C) can enable larger working sets without increasing physical memory capacity. No single technique fits all workloads, and, thus, ETC integrates proactive eviction, memory-aware throttling and capacity compression into a principled framework that dynamically selects the most effective combination of techniques, transparently to the running software. To this end, ETC categorizes applications into three categories: regular applications without data sharing across kernels, regular applications with data sharing across kernels, and irregular applications. Our evaluation shows that ETC fully mitigates the oversubscription overhead for regular applications without data sharing and delivers performance similar to the ideal unlimited GPU memory baseline. We also show that ETC outperforms the state-of-the-art baseline by 60.4% and