Medium and especially fine grain parallelism has been a focus of the data-research area since its inception in the 1970s. Much experience has been gained but the interest both in the academia and the industry continues to flourish. The development of multiple ALU superscalars/superpipelined machines/VLIWs (small resources Von Neumann machines) has meant the related software and hardware topics for finding higher degrees of fine and medium grain parallelism on such machines has become increasingly important. This volume presents new parallelization ideas being discovered in this relatively unchartered research area, including some which are likely to have immediate practical impact. With invited papers from prominent specialists, it is hoped it also offers a critical review of the accomplishments of the data-flow research area to date.
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