It is now well established that the device scaling predicted by Moore's Law is no longer a viable option for increasing the clock frequency of future uniprocessor systems at the rate that had been sustained during the last two decades. As a result, future systems are rapidly moving from uniprocessor to multiprocessor configurations, so as to use parallelism instead of frequency scaling as the foundation for increased compute capacity. The dominant emerging multiprocessor structure for the future is a Non-Uniform Cluster Computing (NUCC) system with nodes that are built out of multi-core SMP chips with non-uniform memory hierarchies, and interconnected in horizontally scalable cluster configurations such as blade servers. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. Current OO language facilities for concurrent and distributed programming are inadequate for addressing the needs of NUCC systems because they do not support the notions of non-uniform data access within a node, or of tight coupling of distributed nodes.We have designed a modern object-oriented programming language, X10, for high performance, high productivity programming of NUCC systems. A member of the partitioned global address space family of languages, X10 highlights the explicit reification of locality in the form of places}; lightweight activities embodied in async, future, foreach, and ateach constructs; a construct for termination detection (finish); the use of lock-free synchronization (atomic blocks); and the manipulation of cluster-wide global data structures. We present an overview of the X10 programming model and language, experience with our reference implementation, and results from some initial productivity comparisons between the X10 and Java™ languages.

Despite the overheads associated with the Java Native Interface (JNI), its opaque and binary-compatible nature make it the preferred interoperability mechanism for Java applications that use legacy, high-performance and architecture-dependent native code. This thesis addresses the performance issues associated with the JNI by providing a strategy that transforms JNI callbacks into semantically equivalent but significantly cheaper operations at runtime. In order to do so, the strategy first inlines native functions into Java applications us-ing a Just-in-time (JIT) compiler. Native function inlining is performed by leveraging the abil-ity to store statically-generated intermediate language alongside native binaries. Once inlined, transformed native code can be further optimized due to the availability of runtime information to the JIT compiler. Preliminary evaluations on a prototype implementation of our strategy show that it can sub-stantially reduce the overhead of performing native calls and JNI callbacks, while preserving the opaque and binary-compatible characteristics of the JNI. ii