Arrays are the ubiquitous organization for indexed data. Throughout programming language evolution, implementations have laid out arrays contiguously in memory. This layout is problematic in space and time. It causes heap fragmentation, garbage collection pauses in proportion to array size, and wasted memory for sparse and over-provisioned arrays. Because of array virtualization in managed languages, an array layout that consists of indirection pointers to fixed-size discontiguous memory blocks can mitigate these problems transparently. This design however incurs significant overhead, but is justified when real-time deadlines and space constraints trump performance. This paper proposes z-rays, a discontiguous array design with flexibility and efficiency. A z-ray has a spine with indirection pointers to fixed-size memory blocks called arraylets, and uses five optimizations: (1) inlining the first N array bytes into the spine, (2) lazy allocation, (3) zero compression, (4) fast array copy, and (5) arraylet copy-on-write. Whereas discontiguous arrays in prior work improve responsiveness and space efficiency, z-rays combine time efficiency and flexibility. On average, the best z-ray configuration performs within 12.7 % of an unmodified Java Virtual Machine on 19 benchmarks, whereas previous designs have two to three times higher overheads. Furthermore, language implementers can configure z-ray optimizations for various design goals. This combination of performance and flexibility creates a better building block for past and future array optimization.

Data races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only after deployment. The frequency and unpredictability of these bugs will only increase as software adds parallelism to exploit multicore hardware. Unfortunately, sound and precise race detectors slow programs by factors of eight or more and do not scale to large numbers of threads. This paper presents a precise, low-overhead sampling-based data race detector called PACER. PACER makes a proportionality guarantee: it detects any race at a rate equal to the sampling rate, by finding races whose first access occurs during a global sampling period. During sampling, PACER tracks all accesses using the dynamically sound and precise FASTTRACK algorithm. In nonsampling periods, PACER discards sampled access information that cannot be part of a reported race, and PACER simplifies tracking of the happens-before relationship, yielding near-constant, instead of linear, overheads. Experimental results confirm our theoretical guarantees. PACER reports races in proportion to the sampling rate. Its time and space overheads scale with the sampling rate, and sampling rates of 1-3 % yield overheads low enough to consider in production software. The resulting system provides a “get what you pay for ” approach that is suitable for identifying real, hard-toreproduce races in deployed systems.

COPYRIGHT 2012 Santosh Ganapati NagarakatteThis dissertation is dedicated to my parents. Without them, this would not have been possible. iii Acknowledgments This dissertation is a direct result of constant support and encouragement from my parents who had more confidence in my abilities than I had, at times, in my ability. Apart from my parents, there are numerous people who have been instrumental in the growth of my research career and my development as an individual. My adviser, Milo Martin has had a transformative influence on me as a researcher. I am fortunate to have worked with him for the last five years. Milo provided me the initial insights, the motivation to work on the problem, and eventually has nourished my ideas. He was generous with his time and wisdom. He provided me an excellent platform where I could excel. Apart from the research under him, he gave me freedom to collaborate with other researchers independently. I have also learned a great deal about teaching, presentations, and mentoring that will be amazingly useful in my future