We describe a highly practical program specializer for Java programs. The specializer is powerful, because it specializes optimistically, using (potentially transient) constants in the heap; it is precise, because it specializes using data structures that are only partially invariant; it is deployable, because it is hidden in a JIT compiler and does not require any user annotations or offline preprocessing; it is simple, because it uses existing JIT compiler ingredients; and it is fast, because it specializes programs in under 1s.These properties are the result of (1) a new algorithm for selecting specializable code fragments, based on a notion of influence; (2) a precise store profile for identifying constant heap locations; and (3) an efficient invalidation mechanism for monitoring optimistic assumptions about heap constants. Our implementation of the specializer in the Jikes RVM has low overhead, selects specialization points that would be chosen manually, and produces speedups ranging from a factor of 1.2 to 6.4, comparable with annotation-guided specializers.

Sophisticated binary translators and dynamic optimizers demand a program profiler with low overhead, high accuracy, and the ability to collect a variety of profile types. A profiling scheme that achieves these goals is proposed. Conceptually, the hardware compresses a stream of profile data by counting identical events; the compressed profile dam is passed to software for analysis. Compressing the high-bandwidth event stream greatly reduces software overhead. Because optimizations can tolerate some profiling errors, we allow the stream compressor to be lossy, thereby enabling a low-cost sampling-based hardware design. Because the hardware compressor is insensitive to the event content, it supports various profile types and can process multiple types simultaneously.

In conventional superscalar microarchitectures with partitioned integer and floating-point resources, all floating-point resources are idle during execution of integer programs. Palacharla and Smith [26] addressed this drawback and proposed that the floating-point subsystem be augmented to support integer operations. The hardware changes required are expected to be fairly minimal.To exploit these idle floating resources, the compiler must identify integer code that can be profitably offloaded to the augmented floating-point subsystem. In this paper, we present two compiler algorithms to do this. The basic scheme offloads integer computation to the floating-point subsystem using existing program loads/stores for inter-partition communication. For the SPECINT95 benchmarks, we show that this scheme offloads from 5% to 29% of the total dynamic instructions to the floating-point subsystem. The advanced scheme inserts copy instructions and duplicates some instructions to further offload computation. We evaluate the effectiveness of the two schemes using timing simulation. We show that the advanced scheme can offload from 9% to 41% of the total dynamic instructions to the floating-point subsystem. In doing so, speedups from 3% to 23% are achieved over a conventional microarchitecture.