Nawaaz Ahmed

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This paper describes a technique for transforming imperfectly-nested loops to enhance locality of reference. The key idea is to embed the iteration space of every statement in the loop nest into a special iteration space called the <i>product</i> space. The product space is interpreted as a perfectly-nested loop that is transformed to enhance locality and(More)
Block recursive codes for dense numerical linear algebra com putations appear to be well suited for execution on machines with deep memory hierarchies because they are e ectively blocked for all levels of the hierarchy In this paper we describe compiler technology to translate iterative versions of a number of numerical kernels into block recursive form We(More)
We present compiler technology for synthesizing sparse matrix code from (i) dense matrix code, and (ii) a description of the index structure of a sparse matrix. Our approach is to embed statement instances into a Cartesian product of statement iteration and data spaces, and to produce efficient sparse code by identifying common enumerations for multiple(More)
It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for(More)
Most numerical applications using arrays require extensive program transformation in order to perform well on current machine architectures with deep memory hierarchies. These transformations ensure that an execution of the application exploits data-locality a n d uses the caches more eeectively. The problem of exploiting data-locality i s w ell understood(More)
We present compiler technology for generating sparse matrix code from (i) dense matrix code and (ii) a description of the indexing structure of the sparse matrices. This technology embeds statement instances into a Cartesian product of statement iteration and data spaces, and produces efficient sparse code by identifying common enumerations for multiple(More)
We present compiler technology for generating sparse matrix code from (i) dense matrix code and (ii) a description of the indexing structure of the sparse matrices. This technology embeds statement instances into a Cartesian product of statement iteration and data spaces, and produces efficient sparse code by identifying common enumerations for multiple(More)