Allow using device memory allocations in debug mode. #758

pratikvn · 2021-05-05T08:35:54Z

Currently, we always use Unified memory for debug builds. While this helps for debugging in some cases, it can be a bit annoying because release and debug builds have different behaviours.

This PR allows the user to specify whether or not to use the allocation using the unified memory. The default behaviour is the same as current behaviour currently controlled by pre-processor defines.

upsj · 2021-05-05T08:49:57Z

I really like the idea and the implementation so far, one thing I would like to avoid though is a function with multiple bool parameters. I also think this isn't necessarily a binary choice, since there are different allocation modes for unified memory (cudaMemAttachGlobal or cudaMemAttachHost)

enum class allocation_mode { device, unified_host, unified_device };

Also, macros should be namespaced GKO_....

codecov · 2021-05-05T10:44:03Z

Codecov Report

Merging #758 (eb9831a) into develop (8212e65) will decrease coverage by 0.00%.
The diff coverage is 100.00%.

❗ Current head eb9831a differs from pull request most recent head 7ff4952. Consider uploading reports for the commit 7ff4952 to get more accurate results

@@             Coverage Diff             @@
##           develop     #758      +/-   ##
===========================================
- Coverage    94.20%   94.19%   -0.01%     
===========================================
  Files          399      399              
  Lines        31030    31027       -3     
===========================================
- Hits         29231    29226       -5     
- Misses        1799     1801       +2

Impacted Files	Coverage Δ
core/device_hooks/cuda_hooks.cpp	`56.25% <100.00%> (ø)`
core/device_hooks/hip_hooks.cpp	`56.25% <100.00%> (ø)`
include/ginkgo/core/base/executor.hpp	`80.31% <100.00%> (+0.21%)`	⬆️
core/test/solver/cb_gmres.cpp	`99.02% <0.00%> (-0.98%)`	⬇️
core/base/extended_float.hpp	`91.26% <0.00%> (-0.98%)`	⬇️
include/ginkgo/core/solver/cb_gmres.hpp	`100.00% <0.00%> (ø)`

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upsj

LGTM!

upsj · 2021-05-05T13:59:01Z

hip/base/executor.hip.cpp

+ if (this->alloc_mode_ == allocation_mode::unified_host) {
+ error_code = hipMallocManaged(&dev_ptr, num_bytes, hipMemAttachHost);
+ } else if (this->alloc_mode_ == allocation_mode::unified_global) {
+ error_code = hipMallocManaged(&dev_ptr, num_bytes, hipMemAttachGlobal);
+ } else if (this->alloc_mode_ == allocation_mode::device) {
+ error_code = hipMalloc(&dev_ptr, num_bytes);


AFAIK ROCm doesn't support unified memory yet, it might make sense to reorder these statements (hipMalloc first) and guard the other two in an #ifdef to retain some of the previous behavior.

does it use the wrong commit? it seemed to contain the @upsj comments yesterday, but does not now

upsj · 2021-05-05T14:01:39Z

include/ginkgo/core/base/executor.hpp

+#define GKO_DEFAULT_CUDA_ALLOC_MODE allocation_mode::device
+
+#define GKO_DEFAULT_HIP_ALLOC_MODE allocation_mode::device


These could also be constexpr global variables

yhmtsai

LGTM in general, but I would like to make sure the cudaMemAttachGlobal things.

cuda/test/base/cuda_executor.cu

yhmtsai · 2021-05-06T16:32:41Z

cuda/test/base/cuda_executor.cu

@@ -95,6 +98,8 @@ protected:
 cuda = gko::CudaExecutor::create(0, omp);
 cuda2 = gko::CudaExecutor::create(
 gko::CudaExecutor::get_num_devices() - 1, omp);
+ cuda3 = gko::CudaExecutor::create(0, omp, false,
+ gko::allocation_mode::unified_host);


should it also test unified_global ?
unified_host require device with cudaDevAttrConcurrentManagedAccess, or, the device can not access the unified_host memory.

Ah, yes, sorry. It was meant to be unified_global .

include/ginkgo/core/base/executor.hpp

yhmtsai

LGTM, is it based on the old commit? some changes seem to be lost.

yhmtsai · 2021-05-07T11:50:21Z

hip/base/executor.hip.cpp

+ if (this->alloc_mode_ == allocation_mode::unified_host) {
+ error_code = hipMallocManaged(&dev_ptr, num_bytes, hipMemAttachHost);
+ } else if (this->alloc_mode_ == allocation_mode::unified_global) {
+ error_code = hipMallocManaged(&dev_ptr, num_bytes, hipMemAttachGlobal);
+ } else if (this->alloc_mode_ == allocation_mode::device) {
+ error_code = hipMalloc(&dev_ptr, num_bytes);


does it use the wrong commit? it seemed to contain the @upsj comments yesterday, but does not now

yhmtsai · 2021-05-07T11:51:50Z

include/ginkgo/core/base/executor.hpp

- static std::shared_ptr<HipExecutor> create(int device_id,
- std::shared_ptr<Executor> master,
- bool device_reset = false);
+ static std::shared_ptr<HipExecutor> create(


Could you also add the documentation for device_reset and alloc_mode of this function? also Cuda

pratikvn · 2021-05-07T12:40:20Z

Yes, sorry, looks like my work laptop was on a older commit and I fell into the peril of the force push.

upsj · 2021-05-07T15:50:52Z

rebase!

+ Add Doc + Use constexpr Co-authored-by: Tobias Ribizel <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

pratikvn · 2021-05-07T16:14:02Z

I guess the rebase does not run the complete CI. But as this was a small PR, should I go ahead and merge this ?

upsj · 2021-05-07T16:19:02Z

For some reason, the rebase doesn't use the @ginkgo-bot token, I guess I'll need to look at that some time. But I would be fine with merging it directly, as it already worked before, and the only change from Thomas' PR was unrelated

sonarcloud · 2021-05-07T17:57:34Z

Kudos, SonarCloud Quality Gate passed!

0 Bugs
0 Vulnerabilities
0 Security Hotspots
2 Code Smells

100.0% Coverage
0.0% Duplication

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

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

pratikvn self-assigned this May 5, 2021

ginkgo-bot added mod:core This is related to the core module. reg:testing This is related to testing. labels May 5, 2021

pratikvn requested review from tcojean, Slaedr, upsj, thoasm and yhmtsai May 5, 2021 08:45

pratikvn removed mod:core This is related to the core module. reg:testing This is related to testing. labels May 5, 2021

pratikvn added 1:ST:WIP This PR is a work in progress. Not ready for review. and removed 1:ST:ready-for-review This PR is ready for review labels May 5, 2021

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pratikvn and others added 3 commits May 7, 2021 15:51

Allow using device memory in debug mode

01acf7e

Update to enum, add different modes

0e838e3

Review update.

7ff4952

+ Add Doc + Use constexpr Co-authored-by: Tobias Ribizel <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

github-actions bot force-pushed the unified-mem branch from eb9831a to 7ff4952 Compare May 7, 2021 15:51

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Allow using device memory allocations in debug mode. #758

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		#define GKO_DEFAULT_CUDA_ALLOC_MODE allocation_mode::device

		#define GKO_DEFAULT_HIP_ALLOC_MODE allocation_mode::device

Allow using device memory allocations in debug mode. #758

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