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Figure 3: Two examples of block partitioning.

Figure 3 Two examples of block partitioning.

Related Figures (10)

Figure 1: Compact storage formats for sparse matrices
Figure 1: Compact storage formats for sparse matrices
Figure 2: Popular algorithms for sparse LU decomposition
Figure 2: Popular algorithms for sparse LU decomposition
Figure 5: Input PE architecture. 5.3 Pivot
Figure 5: Input PE architecture. 5.3 Pivot
Figure 4: The block diagram of our sparse LU decomposition architecture
Figure 4: The block diagram of our sparse LU decomposition architecture
Figure 6: Update PE architecture.
Figure 6: Update PE architecture.
Figure 7: Impact of multiply-accumulate block size and No. of Input matrix partitions on performance.
Figure 7: Impact of multiply-accumulate block size and No. of Input matrix partitions on performance.
Figure 8: Throughput comparison between our architecture, and the FPGA and software implementations in [17].
Figure 8: Throughput comparison between our architecture, and the FPGA and software implementations in [17].
Figure 9: Speedups of our FPGA implementation normal- ized to Matlab’s LU decomposition routine. Table 1: Experimental benchmark matrices, properties, and their performance.
Figure 9: Speedups of our FPGA implementation normal- ized to Matlab’s LU decomposition routine. Table 1: Experimental benchmark matrices, properties, and their performance.
Related topics:
Computer ScienceParallel ComputingMatrix DecompositionSparse MatrixLU decomposition
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