Abstract.A recent trend in mainstream desktop systems is the use of general-purpose graphics processor units (GPGPUs) to obtain order-ofmagnitude performance improvements. CUDA has emerged as a popular programming model for GPGPUs for use by C/C++ programmers. Given the widespread use of modern object-oriented languages with managed runtimes like Java and C#, it is natural to explore how CUDA-like capabilities can be made accessible to those programmers as well. In this paper, we present a programming interface called JCUDA that can be used by Java programmers to invoke CUDA kernels. Using this interface, programmers can write Java codes that directly call CUDA kernels, and delegate the responsibility of generating the Java-CUDA bridge codes and host-device data transfer calls to the compiler. Our preliminary performance results show that this interface can deliver significant performance improvements to Java programmers. For future work, we plan to use the JCUDA interface as a target language for supporting higher level parallel programming languages like X10 and Habanero-Java.

Modern computer systems feature multiple homogeneous or heterogeneous computing units with deep memory hierarchies, and expect a high degree of thread-level parallelism from the software. Exploitation of data locality is critical to achieving scalable parallelism, but adds a significant dimension of complexity to performance optimization of parallel programs. This is especially true for programming models where locality is implicit and opaque to programmers. In this paper, we introduce the hierarchical place tree (HPT) model as a portable abstraction for task parallelism and data movement. The HPT model supports co-allocation of data and computation at multiple levels of a memory hierarchy. It can be viewed as a generalization of concepts from the Sequoia and X10 programming models, resulting in capabilities that are not supported by either. Compared to Sequoia, HPT supports three kinds of data movement in a memory hierarchy rather than just explicit data transfer between adjacent levels, as well as dynamic task scheduling rather than static task assignment. Compared to X10, HPT provides a hierarchical notion of places for both computation and data mapping. We describe our work-in-progress on implementing the HPT model in the Habanero-Java (HJ) compiler and runtime system. Preliminary results on general-purpose multicore processors and GPU accelerators indicate that the HPT model can be a promising portable abstraction for future multicore processors.

Multiple programming models are emerging to address an increased need for dynamic task parallelism in multicore sharedmemory multiprocessors. This poster describes the main components of Rice University's Habanero Multicore Software Research Project, which proposes a new approach to multicore software enablement based on a two-level programming model consisting of a higher-level coordination language for domain experts and a lowerlevel parallel language for programming experts.

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A novel voice conversion system using phoneme-based linear mapping functions on main vowel phonemes is proposed in this paper. Our voice conversion algorithm has the following three improvements. First, instead of using all the Vocal Tract Resonance (VTR) vectors in the portion of a phoneme, we use the VTR vector at the steady-state of each phoneme to train phoneme-based GMM. Second, different linear mapping functions have been trained to describe the mapping relationships for corresponding phonemes. Third, in the transformation procedure, the transformed formant frequencies at the main vowel phonemes are obtained using the corresponding GMM. Besides, prosody parameters are also transformed. Finally the converted speech is resynthesized with the transformed parameters by high quality speech manipulation framework STRAIGHT (Speech Transformation and Representation based on Adaptive Interpolation of weiGHTed spectrogram). Perceptual results for F-M and M-F conversion show that our MOS score of the converted voice is improved from 3.8 to 4.1 and ABX score from 3.3 to 3.8 compared with IBM's system. Comparisons with other systems are also given in this paper.