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- Induprakas Kodukula, Nawaaz Ahmed, Keshav Pingali
- PLDI
- 1997

We present a simple and novel framework for generating blocked codes for high-performance machines with a memory hierarchy. Unlike traditional compiler techniques like tiling, which are based on reasoning about the control flow of programs, our techniques are based on reasoning directly about the flow of data through the memory hierarchy. Our data-centric… (More)

- Nawaaz Ahmed, Nikolay Mateev, Keshav Pingali
- International Journal of Parallel Programming
- 2000

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)

- Nawaaz Ahmed, Keshav Pingali
- Euro-Par
- 2000

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)

- Nawaaz Ahmed, Nikolay Mateev, Keshav Pingali
- SC
- 2000

Tiling is one of the more important transformations for enhancing loca lity of reference in programs. Intuitively, tiling a set of loops achieves the effect of interleaving iterations of these loops. Tiling of perfectly-nested loop nests (which are loop nests in which all assignment statements are contained in the innermost loop) is well understood. In… (More)

- Fuchun Peng, Nawaaz Ahmed, Xin Li, Yumao Lu
- SIGIR
- 2007

Traditionally, stemming has been applied to Information Retrieval tasks by transforming words in documents to the their root form before indexing, and applying a similar transformation to query terms. Although it increases recall, this naive strategy does not work well for Web Search since it lowers precision and requires a significant amount of additional… (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)

- Yumao Lu, Fuchun Peng, Xin Li, Nawaaz Ahmed
- CIKM
- 2006

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)

- Nawaaz Ahmed, Induprakas To, +6 authors Byron For
- 2000

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)