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Avoid doing 64bit division in the page allocator. This causes problems
on 32 bit platforms.
Bug 1799159
Change-Id: I5166a71a4e84454686cce6d6cdca678a862a7ae7
Signed-off-by: Alex Waterman <alexw@nvidia.com>
Reviewed-on: http://git-master/r/1236799
(cherry picked from commit 21c091d1c00433acb9965c3d348d16fbb4c50c1a)
Reviewed-on: http://git-master/r/1236195
Reviewed-by: mobile promotions <svcmobile_promotions@nvidia.com>
Tested-by: mobile promotions <svcmobile_promotions@nvidia.com>
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Add the ability to do "SLAB" allocation in the page allocator. This
is generally useful since the allocator manages 64K pages but often
we only need 4k chunks (for example when allocating memory for page
table entries).
Bug 1799159
JIRA DNVGPU-100
Signed-off-by: Alex Waterman <alexw@nvidia.com>
Reviewed-on: http://git-master/r/1225322
(cherry picked from commit 299a5639243e44be504391d9155b4ae17d914aa2)
Change-Id: Ib3a8558d40ba16bd3a413f4fd38b146beaa3c66b
Reviewed-on: http://git-master/r/1227924
Reviewed-by: mobile promotions <svcmobile_promotions@nvidia.com>
Tested-by: mobile promotions <svcmobile_promotions@nvidia.com>
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Amount of free space in the buddy allocator is computed from the
complete capacity minus currently used bytes.
The page allocator just queries its underlying allocator.
Bug 1787771
Bug 200233138
Change-Id: I9b6f5ef90119236a13de14e14cd0a3ee72144a11
Signed-off-by: Konsta Holtta <kholtta@nvidia.com>
Reviewed-on: http://git-master/r/1223761
(cherry picked from commit 0b324a60ebdf67e793ade869c252a8ddd56c04f8)
Reviewed-on: http://git-master/r/1235979
GVS: Gerrit_Virtual_Submit
Reviewed-by: Terje Bergstrom <tbergstrom@nvidia.com>
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Implement carveout support by just calling through to the buddy
allocator's carveout support.
Jira DNVGPU-84
Change-Id: I1940873394a4cbff0152f1b6c9c4fd659e0076e1
Signed-off-by: Alex Waterman <alexw@nvidia.com>
Reviewed-on: http://git-master/r/1203392
(cherry picked from commit 499ee0407bf525e161a14cfb8bbbc101ac934329)
Reviewed-on: http://git-master/r/1223454
GVS: Gerrit_Virtual_Submit
Reviewed-by: Terje Bergstrom <tbergstrom@nvidia.com>
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When allocating discontiguous memory composed of several chunks,
update also the number of pages used by the current chunk, if a large
chunk was not available and a retry is performed with a smaller one.
Failing to do this would result in too few chunks reserved for a large
enough allocation in certain conditions.
Bug 1805067
Change-Id: I9d14864724d228b42c47eb4669fbe0f789334397
Signed-off-by: Konsta Holtta <kholtta@nvidia.com>
Reviewed-on: http://git-master/r/1214914
(cherry picked from commit 9bece931b13e4dad808622462d4d98d421cfb383)
Reviewed-on: http://git-master/r/1220546
GVS: Gerrit_Virtual_Submit
Reviewed-by: Terje Bergstrom <tbergstrom@nvidia.com>
Tested-by: Terje Bergstrom <tbergstrom@nvidia.com>
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In __gk20a_alloc_pages(), if we fail to allocate a chunk
we free previously allocated chunks in error path
But we do not free up the memory reserved in those chunks
which could lead to OOM situations
Fix this by calling gk20a_free() for each chunk in error
path
Jira DNVGPU-96
Change-Id: I68aa18d68a5282405016e688c790ccbc0c2a0d69
Signed-off-by: Deepak Nibade <dnibade@nvidia.com>
Reviewed-on: http://git-master/r/1203098
(cherry picked from commit f096bd1675600f4e2fc2d686f2911bb945fbbf0b)
Reviewed-on: http://git-master/r/1210952
GVS: Gerrit_Virtual_Submit
Reviewed-by: Terje Bergstrom <tbergstrom@nvidia.com>
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Implement an allocator suitable for managing the video memory on dGPUs.
It works by allocating chunks from an underlying buddy allocator and
collating the chunks together (similar to what an sgt does in the
wider Linux kernel). This handles the ability to get large buffers in
potentially fragmented memory. The GMMU can then obviously map the
physical vidmem into contiguous GVA spaces.
Jira DNVGPU-96
Change-Id: Ic1d7800b033a170b77790aa23fad6858443d0e89
Signed-off-by: Alex Waterman <alexw@nvidia.com>
Reviewed-on: http://git-master/r/1197203
(cherry picked from commit fa44684a843956ae384fef6d7a79b9cbbd04f73e)
Reviewed-on: http://git-master/r/1185231
GVS: Gerrit_Virtual_Submit
Reviewed-by: Terje Bergstrom <tbergstrom@nvidia.com>
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