The need for automatic storage allocation arises from desires for program modularity, machine independence, and resource sharing. Virtual memory is an elegant way of achieving these objectives. In a virtual memory, the addresses a program may use to identify information are distinguished from the addresses the

Garbage collection offers numerous software engineering advantages, but interacts poorly with virtual memory managers. Existing garbage collectors require far more pages than the application's working set and touch pages without regard to which ones are in memory, especially during full-heap garbage collection. The resulting paging can cause throughput to plummet and pause times to spike up to seconds or even minutes. We present a garbage collector that avoids paging. This bookmarking collector cooperates with the virtual memory manager to guide its eviction decisions. Using summary information ("bookmarks") recorded from evicted pages, the collector can perform in-memory full-heap collections. In the absence of memory pressure, the bookmarking collector matches the throughput of the best collector we tested while running in smaller heaps. In the face of memory pressure, it improves throughput by up to a factor of five and reduces pause times by up to a factor of 45 over the next best collector. Compared to a collector that consistently provides high throughput (generational mark-sweep), the bookmarking collector reduces pause times by up to 218x and improves throughput by up to 41x. Bookmarking collection thus provides greater utilization of available physical memory than other collectors while matching or exceeding their throughput.

Computer Science To Sarah for reminding me of everything I can do and to Shoshanna for inspiring me to do more. ACKNOWLEDGMENTS I am most grateful to my advisor, Emery Berger, for everything he has done throughout this thesis. I appreciate his guidance, suggestions, and inspiration. I feel especially fortu-nate for the patience he has shown with me throughout all the twists and turns my life took getting through this dissertation. I must also thank Eliot Moss and Kathryn McKinley for their leadership and support. I will be forever grateful that they took a chance on a student with a less-than-stellar aca-demic record and provided me with a fertile, inspiring research environment. They are both very knowledgeable and I benefited from our discussions in a myriad of ways. They have also served as members of my committee and I appreciate their helpful comments and sug-gestions. Thanks also to Scott Kaplan, another member of my committee, for his advice and feedback.