6 Commits

Author SHA1 Message Date
Sameer Agarwal 0bd9e63dcc Add absl/log/vlog_is_on.h when using VLOG_IS_ON
Previously this header was included in absl/log/log.h
but going forward this header will not be included.

Change-Id: Ie663b221d90e32a852fb00dfe20f731dcd1dcf6b
2024-08-09 16:15:22 -07:00
Sameer Agarwal 0ca2db57c7 Fix a number of typos
Change-Id: I0038f9c91dc70c422c01305dc10fca5a22a2f0d0
2024-07-21 15:15:36 -07:00
Sameer Agarwal 57e26182f3 Add more missing headers
Change-Id: I35875ca856b7b80c622562a1c14b4c8ced10f740
2024-07-18 11:37:07 -07:00
Dmitriy Korchemkin 75bacedf7d CUDA partitioned matrix view
Converts BlockSparseMatrix into two instances of CudaSparseMatrix,
corresponding to left and right sub-matrix.

Values of submatrix E are always just copied as-is, and values of
submatrix F are copied if each row-block of F submatrix satisfies
at least one of the following conditions:
 - There is atmost one cell in row-block
 - Row block has height of 1 row
Otherwise, indices of values in CRS order corresponding to value indices
in block-sparse order are computed on-the-fly.

Change-Id: I14eee00c36ee74b6b83fc85927907641383abfc7
2023-07-14 18:12:20 +00:00
Dmitriy Korchemkin bdee4d6172 Block-sparse to CRS conversion using block-structure
Instead of pre-computing pemutation from block-sparse to CRS order,
index of value in CRS matrix is computed in the process of updating
values using block-sparse structure.

When it is possible to update values via a simple host-to-device copy,
block-sparse structure on GPU is discarded after computing CRS
structure.

Computing index is significantly slower than using pre-computed
permutation, but is still hidden by host-to-device transfer.

On problems from BAL dataset this results into reduction of extra
gpu memory consumption from 33% (permutation stored as 32-bit indices)
to ~10% for storing block-sparse structure.

Benchmark results:

======================= CUDA Device Properties ======================
Cuda version         : 11.8
Device ID            : 0
Device name          : NVIDIA GeForce RTX 2080 Ti
Total GPU memory     :  11012 MiB
GPU memory available :  10852 MiB
Compute capability   : 7.5
Warp size            : 32
Max threads per block: 1024
Max threads per dim  : 1024 1024 64
Max grid size        : 2147483647 65535 65535
Multiprocessor count : 68
====================================================================
Running ./bin/evaluation_benchmark
Run on (112 X 3200 MHz CPU s)
CPU Caches:
  L1 Data 32 KiB (x56)
  L1 Instruction 32 KiB (x56)
  L2 Unified 1024 KiB (x56)
  L3 Unified 39424 KiB (x2)
Load Average: 24.58, 11.75, 8.52

-----------------------------------------------------------------------
Benchmark                                                          Time
-----------------------------------------------------------------------
Using on-the-fly computation of CRS index corresponding to block-sparse
index:

JacobianToCRS<g/final/problem-4585-1324582-pre.txt>             1607 ms
JacobianToCRSView<g/final/problem-4585-1324582-pre.txt>          564 ms
JacobianToCRSMatrix<g/final/problem-4585-1324582-pre.txt>       2226 ms
JacobianToCRSViewUpdate<g/final/problem-4585-1324582-pre.txt>    228 ms
JacobianToCRSMatrixUpdate<g/final/problem-4585-1324582-pre.txt>  400 ms

Using precomputed permutation:
JacobianToCRS</final/problem-4585-1324582-pre.txt>              1656 ms
JacobianToCRSView</final/problem-4585-1324582-pre.txt>           553 ms
JacobianToCRSMatrix</final/problem-4585-1324582-pre.txt>        2255 ms
JacobianToCRSViewUpdate</final/problem-4585-1324582-pre.txt>     228 ms
JacobianToCRSMatrixUpdate</final/problem-4585-1324582-pre.txt>   406 ms

Performance of JacobianToCRSViewUpdate is still limited by
host-to-device transfer, and JacobianToCRSView is faster than computing
CRS structure on CPU.

Change-Id: Ifb6910fb01ae6071400d36c277846fadc5857964
2023-05-26 01:12:47 +03:00
Dmitriy Korchemkin e7bd72d41e Permutation-based conversion from block-sparse to crs
Change-Id: Ic33a6476c033187dff61886deb6d1761524943f0
2023-05-12 03:33:25 +03:00