ABSTRACT Computing an equi-join followed by a duplicate eliminating projection is conventionally done by performing the two operations in serial. If some join attribute is projected away the intermediate result may be much larger than both the input and the output, and the computation could therefore potentially be performed faster by a direct procedure that does not produce such a large intermediate result. We present a new algorithm that has smaller intermediate results on worst-case inputs, and in particular is more efficient in both the RAM and I/O model. It is easy to see that join-project where the join attributes are projected away is equivalent to boolean matrix multiplication. Our results can therefore also be interpreted as improv...
131 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.The second problem we address...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the s...
A Join-Project operation is a join operation followed by a duplicate eliminating projection operatio...
We consider the problem of sparse matrix multiplication by the column row method in a distributed se...
Two new algorithms, "Jive-join" and "Slam-join," are proposed for computing the ...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
Parallel join algorithms have received much attention in recent years, due to the rapid development ...
Most join algorithms can be extended to reduce wasted work when several tuples contain the same valu...
Most join algorithms can be extended to reduce wasted work when several tuples contain the same valu...
We study algorithms for computing the equijoin of two relations in B system with a standard architec...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the perform...
Combinatorial scientific computing plays an important enabling role in computational science, partic...
131 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.The second problem we address...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the s...
A Join-Project operation is a join operation followed by a duplicate eliminating projection operatio...
We consider the problem of sparse matrix multiplication by the column row method in a distributed se...
Two new algorithms, "Jive-join" and "Slam-join," are proposed for computing the ...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
Parallel join algorithms have received much attention in recent years, due to the rapid development ...
Most join algorithms can be extended to reduce wasted work when several tuples contain the same valu...
Most join algorithms can be extended to reduce wasted work when several tuples contain the same valu...
We study algorithms for computing the equijoin of two relations in B system with a standard architec...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
Sparse matrix operations dominate the cost of many scientific applications. In parallel, the perform...
Combinatorial scientific computing plays an important enabling role in computational science, partic...
131 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.The second problem we address...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the s...