Now showing items 1-6 of 6

    • Compiling Parallel Sparse Code for User-Defined Data Structures 

      Kotlyar, Vladimir; Pingali, Keshav; Stodghill, Paul (Cornell University, 1997-06)
      We describe how various sparse matrix and distribution formats can be handled using the {\em relational} approach to sparse matrix code compilation. This approach allows for the development of compilation techniques that ...
    • A Generic Programming System for Sparse Matrix Computations 

      Mateev, Nikolay; Kotlyar, Vladimir; Pingali, Keshav; Stodghill, Paul (Cornell University, 1999-07)
      Sparse matrices are stored in compressed formats in which zeros are not stored explicitly. Writing high-performance sparse matrix libraries is a difficult and tedious job because there are many compressed formats in use ...
    • Relational Algebraic Techniques for the Synthesis of Sparse MatrixPrograms 

      Kotlyar, Vladimir (Cornell University, 1999-02)
      Sparse matrix computations are ubiquitous in computational science. However, the development of high-performance software for sparse matrix computations is a tedious and error-prone task, for two reasons. First, there is ...
    • A Relational Approach to the Compilation of Sparse Matrix Programs 

      Kotlyar, Vladimir; Pingali, Keshav; Stodghill, Paul (Cornell University, 1997-03)
      We present a relational algebra based framework for compiling efficient sparse matrix code from dense DO-ANY loops and a specification of the representation of the sparse matrix. We present experimental data that demonstrates ...
    • Solving Alignment using Elementary Linear Algebra 

      Bau, David; Kodukula, Induprakas; Kotlyar, Vladimir; Pingali, Keshav; Stodghill, Paul (Cornell University, 1995-01)
      Data and computation alignment is an important part of compiling sequential programs to architectures with non-uniform memory access times. In this paper, we show that elementary matrix methods can be used to determine ...
    • Unified framework for sparse and dense SPMD code generation(preliminary report) 

      Kotlyar, Vladimir; Pingali, Keshav; Stodghill, Paul (Cornell University, 1997-03)
      We describe a novel approach to sparse {\em and} dense SPMD code generation: we view arrays (sparse and dense) as distributed relations and parallel loop execution as distributed relational query evaluation. This approach ...