Sparse block performance comparison

This file contains a short evaluation of different sparse blocks. We included one dense block (DenseBlockFP64) as a baseline.

Execution time

We measured the performance of two matrices being multiplied, both of the corresponding block type. Before each measurement, we performed 30 warmup runs (performing 30 matrix multiplications to random matrices without measuring). Then, we measured the delta nanos of the operation below 100 times and took the average. For this, we selected a block size of 1024x1024.

MatrixBlock m3 = m1.aggregateBinaryOperations(m1, m2,
                new MatrixBlock(), InstructionUtils.getMatMultOperator(1));

Storage

The image below shows the memory consumption of each block type for different sparsities. This was evaluated using blocks with 1024 rows and 1024 columns.

We also evaluated on different matrix shapes by fixing the number of rows and manipulating the number of columns. These tests were repeated for different sparsities.

To get more meaningful results, the next evaluation fixes the number of entries in a matrix to 1024x1024 and adapts the column-to-row ratio (and vice versa) accordingly. We define the ratio as cl = (1 + ratio) * rl if ratio >= 0, otherwise as rl = (1 - ratio) * cl. If we have for example a ratio=1, this means that there are twice as many columns as rows. If the ratio=-1, this means that there are twice as many rows as columns. Each test with a specific ratio was repeated 10 times with different random matrices and we took the average memory consumption. The images below show our results for different sparsities.