@inproceedings{han_liu_tuck_2010, title={Speculative parallelization of partial reduction variables}, DOI={10.1145/1772954.1772975}, abstractNote={Reduction variables are an important class of cross-thread dependence that can be parallelized by exploiting the associativity and commutativity of their operation. In this paper, we define a class of shared variables called partial reduction variables (PRV). These variables either cannot be proven to be reductions or they violate the requirements of a reduction variable in some way.
 We describe an algorithm that allows the compiler to detect PRVs, and we also discuss the necessary requirements to parallelize detected PRVs. Based on these requirements, we propose an implementation in a TLS system to parallelize PRVs that works by a combination of techniques at compile time and in the hardware. The compiler transforms the variable under the assumption that the reduction-like behavior proven statically will hold true at runtime. However, if a thread reads or updates the shared variable as a result of an alias or unlikely control path, a lightweight hardware mechanism will detect the access and synchronize it to ensure correct execution. We implement our compiler analysis and transformation in GCC, and analyze its potential on the SPEC CPU 2000 benchmarks.We find that supporting PRVs provides up to 46% performance gain over a highly optimized TLS system and on average 10.7% performance improvement.}, booktitle={International Symposium on Code Generation and Optimization}, author={Han, L. A. and Liu, W. and Tuck, J. M.}, year={2010}, pages={141–150} }
 @article{jin_liu_scordilis_han_2010, title={Speech Enhancement Using Harmonic Emphasis and Adaptive Comb Filtering}, volume={18}, ISSN={["1558-7924"]}, DOI={10.1109/TASL.2009.2028916}, abstractNote={An enhancement method for single-channel speech degraded by additive noise is proposed. A spectral weighting function is derived by constrained optimization to suppress noise in the frequency domain. Two design parameters are included in the suppression gain, namely, the frequency-dependent noise-flooring parameter (FDNFP) and the gain factor. The FDNFP controls the level of admissible residual noise in the enhanced speech. Enhanced harmonic structures are incorporated into the FDNFP by time-domain processing of the linear prediction residuals of voiced speech. Further enhancement of the harmonics is achieved by adaptive comb filtering derived using the gain factor with a peak-picking algorithm. The performance of the enhancement method was evaluated by the modified bark spectral distance (MBSD), ITU-Perceptual Evaluation of Speech Quality (PESQ) scores, composite objective measures and listening tests. Experimental results indicate that the proposed method outperforms spectral subtraction; a main signal subspace method applicable to both white and colored noise conditions and a perceptually based enhancement method with a constant noise-flooring parameter, particularly at lower signal-to-noise ratio conditions. Our listening test indicated that 16 listeners on average preferred the proposed approach over any of the other three approaches about 73% of the time.}, number={2}, journal={IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING}, author={Jin, Wen and Liu, Xin and Scordilis, Michael S. and Han, Lu}, year={2010}, month={Feb}, pages={356–368} }