[SYSTEMDS-1752] Cache-conscious mmchain matrix multiply for wide matrices

• Type: Task
• Status: Closed
• Priority: Major
• Resolution: Done
• Fix Version/s: SystemML 0.15
• Assignee: Matthias Boehm
• Reporter: Matthias Boehm
• Created: 08/Jul/17 07:57
• Updated: 09/Sep/17 02:09
• Resolved: 09/Jul/17 06:25

Description

The fused mmchain matrix multiply for patterns such as t(X) %% (w * (X %% v)) uses row-wise dotProduct and vectMultAdd operations, which works very well for the common case of tall&skinny matrices where individual rows fit into L1 cache. However, for graph and text scenarios with wide matrices this leads to cache trashing on the input and output vectors.

This task aims to generalize these dense and sparse operations to perform the computation in a cache-conscious manner when necessary, by accessing fragments of the input and output vector for groups of rows. For dense this is trivial to realize while for sparse it requires a careful determination of the block sizes according to the input sparsity.

Issue Links

• relates to: SYSTEMDS-913 Performance matrix-vector multiplication w/ tall rhs vector