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Work around ILU and IC use-after-free on CUDA 11.4 #846

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@upsj upsj commented Jul 23, 2021

This PR works around an issue in CUDA 11.4 probably caused by the ILU info destruction not waiting for the previous kernels.

@upsj upsj requested a review from a team July 23, 2021 11:51
@upsj upsj self-assigned this Jul 23, 2021
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LGTM. does error message have specific information to point here?
is use-after-free cases on info, desc, or temporary workspace?

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upsj commented Jul 23, 2021

From the bug report I wrote:

The following sequence of calls causes an illegal memory access
cusparseCreateMatDescr
cusparseCreateCsric02Info
cusparseDcsric02_bufferSize
cusparseDcsric02_analysis with CUSPARSE_SOLVE_POLICY_USE_LEVEL
cusparseDcsric02 with CUSPARSE_SOLVE_POLICY_USE_LEVEL
cusparseDestroyCsric02Info
cusparseDestroyMatDescr

If I put a cudaDeviceSynchronize call before cusparseDestroyCsric02Info, the issue goes away, so most likely cusparseDestroyCsric02Info does not wait on the previous kernel to finish before freeing its memory.
The corresponding code worked and produced correct results up to CUDA 11.3

Putting the synchronization at any later point causes the code to fail still.

@ginkgo-bot ginkgo-bot added mod:cuda This is related to the CUDA module. type:factorization This is related to the Factorizations labels Jul 23, 2021
@upsj upsj added 1:ST:ready-to-merge This PR is ready to merge. and removed mod:cuda This is related to the CUDA module. type:factorization This is related to the Factorizations labels Jul 23, 2021
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upsj commented Jul 23, 2021

The bug was confirmed, though it seems to happen only on Turing, not on Volta

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codecov bot commented Jul 23, 2021

Codecov Report

Merging #846 (2d83d2e) into develop (a0b5421) will decrease coverage by 0.30%.
The diff coverage is n/a.

❗ Current head 2d83d2e differs from pull request most recent head 2c7f8ac. Consider uploading reports for the commit 2c7f8ac to get more accurate results
Impacted file tree graph

@@             Coverage Diff             @@
##           develop     #846      +/-   ##
===========================================
- Coverage    94.56%   94.25%   -0.31%     
===========================================
  Files          410      408       -2     
  Lines        32995    32756     -239     
===========================================
- Hits         31201    30874     -327     
- Misses        1794     1882      +88     
Impacted Files Coverage Δ
core/factorization/ilu.cpp 0.00% <0.00%> (-100.00%) ⬇️
reference/factorization/ic_kernels.cpp 0.00% <0.00%> (-100.00%) ⬇️
reference/factorization/ilu_kernels.cpp 0.00% <0.00%> (-100.00%) ⬇️
include/ginkgo/core/factorization/ilu.hpp 0.00% <0.00%> (-100.00%) ⬇️
core/factorization/ic.cpp 0.00% <0.00%> (-96.88%) ⬇️
include/ginkgo/core/factorization/ic.hpp 0.00% <0.00%> (-93.34%) ⬇️
reference/test/factorization/par_ic_kernels.cpp 91.66% <0.00%> (ø)
reference/test/factorization/ic_kernels.cpp
reference/test/factorization/ilu_kernels.cpp
omp/reorder/rcm_kernels.cpp 98.13% <0.00%> (+0.60%) ⬆️

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upsj commented Jul 23, 2021

rebase!

@upsj upsj merged commit e554601 into develop Jul 23, 2021
@upsj upsj deleted the fix_cuda_11_4_ilu branch July 23, 2021 22:13
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sonarcloud bot commented Jul 24, 2021

Kudos, SonarCloud Quality Gate passed!    Quality Gate passed

Bug A 0 Bugs
Vulnerability A 0 Vulnerabilities
Security Hotspot A 0 Security Hotspots
Code Smell A 0 Code Smells

No Coverage information No Coverage information
No Duplication information No Duplication information

tcojean added a commit that referenced this pull request Aug 20, 2021
Ginkgo release 1.4.0

The Ginkgo team is proud to announce the new Ginkgo minor release 1.4.0. This
release brings most of the Ginkgo functionality to the Intel DPC++ ecosystem
which enables Intel-GPU and CPU execution. The only Ginkgo features which have
not been ported yet are some preconditioners.

Ginkgo's mixed-precision support is greatly enhanced thanks to:
1. The new Accessor concept, which allows writing kernels featuring on-the-fly
memory compression, among other features. The accessor can be used as
header-only, see the [accessor BLAS benchmarks repository](https://github.com/ginkgo-project/accessor-BLAS/tree/develop) as a usage example.
2. All LinOps now transparently support mixed-precision execution. By default,
this is done through a temporary copy which may have a performance impact but
already allows mixed-precision research.

Native mixed-precision ELL kernels are implemented which do not see this cost.
The accessor is also leveraged in a new CB-GMRES solver which allows for
performance improvements by compressing the Krylov basis vectors. Many other
features have been added to Ginkgo, such as reordering support, a new IDR
solver, Incomplete Cholesky preconditioner, matrix assembly support (only CPU
for now), machine topology information, and more!

Supported systems and requirements:
+ For all platforms, cmake 3.13+
+ C++14 compliant compiler
+ Linux and MacOS
  + gcc: 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + clang: 3.9+
  + Intel compiler: 2018+
  + Apple LLVM: 8.0+
  + CUDA module: CUDA 9.0+
  + HIP module: ROCm 3.5+
  + DPC++ module: Intel OneAPI 2021.3. Set the CXX compiler to `dpcpp`.
+ Windows
  + MinGW and Cygwin: gcc 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + Microsoft Visual Studio: VS 2019
  + CUDA module: CUDA 9.0+, Microsoft Visual Studio
  + OpenMP module: MinGW or Cygwin.


Algorithm and important feature additions:
+ Add a new DPC++ Executor for SYCL execution and other base utilities
  [#648](#648), [#661](#661), [#757](#757), [#832](#832)
+ Port matrix formats, solvers and related kernels to DPC++. For some kernels,
  also make use of a shared kernel implementation for all executors (except
  Reference). [#710](#710), [#799](#799), [#779](#779), [#733](#733), [#844](#844), [#843](#843), [#789](#789), [#845](#845), [#849](#849), [#855](#855), [#856](#856)
+ Add accessors which allow multi-precision kernels, among other things.
  [#643](#643), [#708](#708)
+ Add support for mixed precision operations through apply in all LinOps. [#677](#677)
+ Add incomplete Cholesky factorizations and preconditioners as well as some
  improvements to ILU. [#672](#672), [#837](#837), [#846](#846)
+ Add an AMGX implementation and kernels on all devices but DPC++.
  [#528](#528), [#695](#695), [#860](#860)
+ Add a new mixed-precision capability solver, Compressed Basis GMRES
  (CB-GMRES). [#693](#693), [#763](#763)
+ Add the IDR(s) solver. [#620](#620)
+ Add a new fixed-size block CSR matrix format (for the Reference executor).
  [#671](#671), [#730](#730)
+ Add native mixed-precision support to the ELL format. [#717](#717), [#780](#780)
+ Add Reverse Cuthill-McKee reordering [#500](#500), [#649](#649)
+ Add matrix assembly support on CPUs. [#644](#644)
+ Extends ISAI from triangular to general and spd matrices. [#690](#690)

Other additions:
+ Add the possibility to apply real matrices to complex vectors.
  [#655](#655), [#658](#658)
+ Add functions to compute the absolute of a matrix format. [#636](#636)
+ Add symmetric permutation and improve existing permutations.
  [#684](#684), [#657](#657), [#663](#663)
+ Add a MachineTopology class with HWLOC support [#554](#554), [#697](#697)
+ Add an implicit residual norm criterion. [#702](#702), [#818](#818), [#850](#850)
+ Row-major accessor is generalized to more than 2 dimensions and a new
  "block column-major" accessor has been added. [#707](#707)
+ Add an heat equation example. [#698](#698), [#706](#706)
+ Add ccache support in CMake and CI. [#725](#725), [#739](#739)
+ Allow tuning and benchmarking variables non intrusively. [#692](#692)
+ Add triangular solver benchmark [#664](#664)
+ Add benchmarks for BLAS operations [#772](#772), [#829](#829)
+ Add support for different precisions and consistent index types in benchmarks.
  [#675](#675), [#828](#828)
+ Add a Github bot system to facilitate development and PR management.
  [#667](#667), [#674](#674), [#689](#689), [#853](#853)
+ Add Intel (DPC++) CI support and enable CI on HPC systems. [#736](#736), [#751](#751), [#781](#781)
+ Add ssh debugging for Github Actions CI. [#749](#749)
+ Add pipeline segmentation for better CI speed. [#737](#737)


Changes:
+ Add a Scalar Jacobi specialization and kernels. [#808](#808), [#834](#834), [#854](#854)
+ Add implicit residual log for solvers and benchmarks. [#714](#714)
+ Change handling of the conjugate in the dense dot product. [#755](#755)
+ Improved Dense stride handling. [#774](#774)
+ Multiple improvements to the OpenMP kernels performance, including COO,
an exclusive prefix sum, and more. [#703](#703), [#765](#765), [#740](#740)
+ Allow specialization of submatrix and other dense creation functions in solvers. [#718](#718)
+ Improved Identity constructor and treatment of rectangular matrices. [#646](#646)
+ Allow CUDA/HIP executors to select allocation mode. [#758](#758)
+ Check if executors share the same memory. [#670](#670)
+ Improve test install and smoke testing support. [#721](#721)
+ Update the JOSS paper citation and add publications in the documentation.
  [#629](#629), [#724](#724)
+ Improve the version output. [#806](#806)
+ Add some utilities for dim and span. [#821](#821)
+ Improved solver and preconditioner benchmarks. [#660](#660)
+ Improve benchmark timing and output. [#669](#669), [#791](#791), [#801](#801), [#812](#812)


Fixes:
+ Sorting fix for the Jacobi preconditioner. [#659](#659)
+ Also log the first residual norm in CGS [#735](#735)
+ Fix BiCG and HIP CSR to work with complex matrices. [#651](#651)
+ Fix Coo SpMV on strided vectors. [#807](#807)
+ Fix segfault of extract_diagonal, add short-and-fat test. [#769](#769)
+ Fix device_reset issue by moving counter/mutex to device. [#810](#810)
+ Fix `EnableLogging` superclass. [#841](#841)
+ Support ROCm 4.1.x and breaking HIP_PLATFORM changes. [#726](#726)
+ Decreased test size for a few device tests. [#742](#742)
+ Fix multiple issues with our CMake HIP and RPATH setup.
  [#712](#712), [#745](#745), [#709](#709)
+ Cleanup our CMake installation step. [#713](#713)
+ Various simplification and fixes to the Windows CMake setup. [#720](#720), [#785](#785)
+ Simplify third-party integration. [#786](#786)
+ Improve Ginkgo device arch flags management. [#696](#696)
+ Other fixes and improvements to the CMake setup.
  [#685](#685), [#792](#792), [#705](#705), [#836](#836)
+ Clarification of dense norm documentation [#784](#784)
+ Various development tools fixes and improvements [#738](#738), [#830](#830), [#840](#840)
+ Make multiple operators/constructors explicit. [#650](#650), [#761](#761)
+ Fix some issues, memory leaks and warnings found by MSVC.
  [#666](#666), [#731](#731)
+ Improved solver memory estimates and consistent iteration counts [#691](#691)
+ Various logger improvements and fixes [#728](#728), [#743](#743), [#754](#754)
+ Fix for ForwardIterator requirements in iterator_factory. [#665](#665)
+ Various benchmark fixes. [#647](#647), [#673](#673), [#722](#722)
+ Various CI fixes and improvements. [#642](#642), [#641](#641), [#795](#795), [#783](#783), [#793](#793), [#852](#852)


Related PR: #857
tcojean added a commit that referenced this pull request Aug 23, 2021
Release 1.4.0 to master

The Ginkgo team is proud to announce the new Ginkgo minor release 1.4.0. This
release brings most of the Ginkgo functionality to the Intel DPC++ ecosystem
which enables Intel-GPU and CPU execution. The only Ginkgo features which have
not been ported yet are some preconditioners.

Ginkgo's mixed-precision support is greatly enhanced thanks to:
1. The new Accessor concept, which allows writing kernels featuring on-the-fly
memory compression, among other features. The accessor can be used as
header-only, see the [accessor BLAS benchmarks repository](https://github.com/ginkgo-project/accessor-BLAS/tree/develop) as a usage example.
2. All LinOps now transparently support mixed-precision execution. By default,
this is done through a temporary copy which may have a performance impact but
already allows mixed-precision research.

Native mixed-precision ELL kernels are implemented which do not see this cost.
The accessor is also leveraged in a new CB-GMRES solver which allows for
performance improvements by compressing the Krylov basis vectors. Many other
features have been added to Ginkgo, such as reordering support, a new IDR
solver, Incomplete Cholesky preconditioner, matrix assembly support (only CPU
for now), machine topology information, and more!

Supported systems and requirements:
+ For all platforms, cmake 3.13+
+ C++14 compliant compiler
+ Linux and MacOS
  + gcc: 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + clang: 3.9+
  + Intel compiler: 2018+
  + Apple LLVM: 8.0+
  + CUDA module: CUDA 9.0+
  + HIP module: ROCm 3.5+
  + DPC++ module: Intel OneAPI 2021.3. Set the CXX compiler to `dpcpp`.
+ Windows
  + MinGW and Cygwin: gcc 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + Microsoft Visual Studio: VS 2019
  + CUDA module: CUDA 9.0+, Microsoft Visual Studio
  + OpenMP module: MinGW or Cygwin.


Algorithm and important feature additions:
+ Add a new DPC++ Executor for SYCL execution and other base utilities
  [#648](#648), [#661](#661), [#757](#757), [#832](#832)
+ Port matrix formats, solvers and related kernels to DPC++. For some kernels,
  also make use of a shared kernel implementation for all executors (except
  Reference). [#710](#710), [#799](#799), [#779](#779), [#733](#733), [#844](#844), [#843](#843), [#789](#789), [#845](#845), [#849](#849), [#855](#855), [#856](#856)
+ Add accessors which allow multi-precision kernels, among other things.
  [#643](#643), [#708](#708)
+ Add support for mixed precision operations through apply in all LinOps. [#677](#677)
+ Add incomplete Cholesky factorizations and preconditioners as well as some
  improvements to ILU. [#672](#672), [#837](#837), [#846](#846)
+ Add an AMGX implementation and kernels on all devices but DPC++.
  [#528](#528), [#695](#695), [#860](#860)
+ Add a new mixed-precision capability solver, Compressed Basis GMRES
  (CB-GMRES). [#693](#693), [#763](#763)
+ Add the IDR(s) solver. [#620](#620)
+ Add a new fixed-size block CSR matrix format (for the Reference executor).
  [#671](#671), [#730](#730)
+ Add native mixed-precision support to the ELL format. [#717](#717), [#780](#780)
+ Add Reverse Cuthill-McKee reordering [#500](#500), [#649](#649)
+ Add matrix assembly support on CPUs. [#644](#644)
+ Extends ISAI from triangular to general and spd matrices. [#690](#690)

Other additions:
+ Add the possibility to apply real matrices to complex vectors.
  [#655](#655), [#658](#658)
+ Add functions to compute the absolute of a matrix format. [#636](#636)
+ Add symmetric permutation and improve existing permutations.
  [#684](#684), [#657](#657), [#663](#663)
+ Add a MachineTopology class with HWLOC support [#554](#554), [#697](#697)
+ Add an implicit residual norm criterion. [#702](#702), [#818](#818), [#850](#850)
+ Row-major accessor is generalized to more than 2 dimensions and a new
  "block column-major" accessor has been added. [#707](#707)
+ Add an heat equation example. [#698](#698), [#706](#706)
+ Add ccache support in CMake and CI. [#725](#725), [#739](#739)
+ Allow tuning and benchmarking variables non intrusively. [#692](#692)
+ Add triangular solver benchmark [#664](#664)
+ Add benchmarks for BLAS operations [#772](#772), [#829](#829)
+ Add support for different precisions and consistent index types in benchmarks.
  [#675](#675), [#828](#828)
+ Add a Github bot system to facilitate development and PR management.
  [#667](#667), [#674](#674), [#689](#689), [#853](#853)
+ Add Intel (DPC++) CI support and enable CI on HPC systems. [#736](#736), [#751](#751), [#781](#781)
+ Add ssh debugging for Github Actions CI. [#749](#749)
+ Add pipeline segmentation for better CI speed. [#737](#737)


Changes:
+ Add a Scalar Jacobi specialization and kernels. [#808](#808), [#834](#834), [#854](#854)
+ Add implicit residual log for solvers and benchmarks. [#714](#714)
+ Change handling of the conjugate in the dense dot product. [#755](#755)
+ Improved Dense stride handling. [#774](#774)
+ Multiple improvements to the OpenMP kernels performance, including COO,
an exclusive prefix sum, and more. [#703](#703), [#765](#765), [#740](#740)
+ Allow specialization of submatrix and other dense creation functions in solvers. [#718](#718)
+ Improved Identity constructor and treatment of rectangular matrices. [#646](#646)
+ Allow CUDA/HIP executors to select allocation mode. [#758](#758)
+ Check if executors share the same memory. [#670](#670)
+ Improve test install and smoke testing support. [#721](#721)
+ Update the JOSS paper citation and add publications in the documentation.
  [#629](#629), [#724](#724)
+ Improve the version output. [#806](#806)
+ Add some utilities for dim and span. [#821](#821)
+ Improved solver and preconditioner benchmarks. [#660](#660)
+ Improve benchmark timing and output. [#669](#669), [#791](#791), [#801](#801), [#812](#812)


Fixes:
+ Sorting fix for the Jacobi preconditioner. [#659](#659)
+ Also log the first residual norm in CGS [#735](#735)
+ Fix BiCG and HIP CSR to work with complex matrices. [#651](#651)
+ Fix Coo SpMV on strided vectors. [#807](#807)
+ Fix segfault of extract_diagonal, add short-and-fat test. [#769](#769)
+ Fix device_reset issue by moving counter/mutex to device. [#810](#810)
+ Fix `EnableLogging` superclass. [#841](#841)
+ Support ROCm 4.1.x and breaking HIP_PLATFORM changes. [#726](#726)
+ Decreased test size for a few device tests. [#742](#742)
+ Fix multiple issues with our CMake HIP and RPATH setup.
  [#712](#712), [#745](#745), [#709](#709)
+ Cleanup our CMake installation step. [#713](#713)
+ Various simplification and fixes to the Windows CMake setup. [#720](#720), [#785](#785)
+ Simplify third-party integration. [#786](#786)
+ Improve Ginkgo device arch flags management. [#696](#696)
+ Other fixes and improvements to the CMake setup.
  [#685](#685), [#792](#792), [#705](#705), [#836](#836)
+ Clarification of dense norm documentation [#784](#784)
+ Various development tools fixes and improvements [#738](#738), [#830](#830), [#840](#840)
+ Make multiple operators/constructors explicit. [#650](#650), [#761](#761)
+ Fix some issues, memory leaks and warnings found by MSVC.
  [#666](#666), [#731](#731)
+ Improved solver memory estimates and consistent iteration counts [#691](#691)
+ Various logger improvements and fixes [#728](#728), [#743](#743), [#754](#754)
+ Fix for ForwardIterator requirements in iterator_factory. [#665](#665)
+ Various benchmark fixes. [#647](#647), [#673](#673), [#722](#722)
+ Various CI fixes and improvements. [#642](#642), [#641](#641), [#795](#795), [#783](#783), [#793](#793), [#852](#852)

Related PR: #866
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