No abstract

Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern high-performance applications. Highly-tuned general purpose allocators have per-operation costs around one hundred cycles, while the cost of an operation in a custom memory allocator can be just a handful of cycles. To achieve high performance, programmers often write custom memory allocators from scratch -a difficult and error-prone process.In this paper, we present a flexible and efficient infrastructure for building memory allocators that is based on C++ templates and inheritance. This novel approach allows programmers to build custom and general-purpose allocators as "heap layers" that can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies allocator construction and results in allocators that either match or improve the performance of heavily-tuned allocators written in C, including the Kingsley allocator and the GNU obstack library. We further show this infrastructure can be used to rapidly build a general-purpose allocator that has performance comparable to the Lea allocator, one of the best uniprocessor allocators available. We thus demonstrate a clean, easy-to-use allocator interface that seamlessly combines the power and efficiency of any number of general and custom allocators within a single application.

A boundary element method for solving three-dimensional linear elasticity problems that involve a large number of particles embedded in a binder is introduced. The proposed method relies on an iterative solution strategy in which matrix-vector multiplication is performed with the fast multipole method. As a result the method is capable of solving problems with N unknowns using only O(N) memory and O(N) operations. Results are given for problems with hundreds of particles in which N"O(10).

No abstract

Garbage collection offers numerous software engineering advantages, but it interacts poorly with virtual memory managers. Most existing garbage collectors visit many more pages than the application itself 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. It records summary information ("bookmarks") about evicted pages that enables it to 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.