The X10 programming language is intended to ease the programming of scalable concurrent and distributed applications. X10 augments a familiar imperative object-oriented programming model with constructs to support light-weight asynchronous tasks as well as execution across multiple address spaces. A crucial aspect of X10's runtime system is the scheduling of concurrent tasks. Work-stealing schedulers have been shown to efficiently load balance fine-grain divide-and-conquer task-parallel program on SMPs and multicores. But X10 is not limited to shared-memory fork-join parallelism. X10 permits tasks to suspend and synchronize by means of conditional atomic blocks and remote task invocations. In this paper, we demonstrate that work-stealing scheduling principles are applicable to a rich programming language such as X10, achieving performance at scale without compromising expressivity, ease of use, or portability. We design and implement a portable work-stealing execution engine for X10. While this engine is biased toward the efficient execution of fork-join parallelism in shared memory, it handles the full X10 language, especially conditional atomic blocks and distribution. We show that this engine improves the run time of a series of benchmark programs by several orders of magnitude when used in combination with the C++ backend compiler and runtime for X10. It achieves scaling comparable to state-of-the art work-stealing scheduler implementations---the Cilk++ compiler and the Java fork/join framework---despite the dramatic increase in generality.

R is a popular dynamic language designed for statistical computing. Despite R's huge user base, the inefficiency in R's language implementation becomes a major pain-point in everyday use as well as an obstacle to apply R to solve large scale analytics problems. The two most common approaches to improve the performance of dynamic languages are: implementing more efficient interpretation strategies and extending the interpreter with Just-In-Time (JIT) compiler. However, both approaches require significant changes to the interpreter, and complicate the adoption by development teams as a result. This paper presents a new approach to improve execution efficiency of R programs by vectorizing the widely used Apply class of operations. Apply accepts two parameters: a function and a collection of input data elements. The standard implementation of Apply iteratively invokes the input function with each element in the data collection. Our approach combines data transformation and function vectorization to convert the looping-over-data execution of the standard Apply into a single invocation of a vectorized function that contains a sequence of vector operations over the input data. This conversion can significantly speed-up the execution of Apply operations in R by reducing the number of interpretation steps. We implemented the vectorization transformation as an R package. To enable the optimization, all that is needed is to invoke the package, and the user can use a normal R interpreter without any changes. The evaluation shows that the proposed method delivers significant performance improvements for a collection of data analysis algorithm benchmarks. This is achieved without any native code generation and using only a single-thread of execution.