GitLab

* Remove std::enable_if, warpRotateLeft is always used with TILE_SIZE

* Do not use built-in warpSize in constexpr contexts

Starting from ROCm 7 warpSize is no longer constexpr.
findInteractingBlocks.hip uses it for sizes of __shared__ arrays.

* Check if hipHostMallocNumaUser is allowed before using it

* Replace SimTK-containing file headers

* Update file headers for new Tinker reader files added

* Initial implementation of C++ API

* Add kernel interface and information for API generation

* API updates for updating electrode parameters

* Add serialization proxy for ConstantPotentialForce

* Update file headers

* Add CG error tolerance and fix units on getCharges() return value

* Initial implementation of matrix solver

* Fixes and conjugate gradient solver

* Try to fix Linux and Windows builds

* Make sure charge constraint target is on total charge

* Restore handling of exceptions like NonbondedForce since they won't involve electrode atoms

* Ameliorate numerical instability in constrained conjugate gradient

* Fix uninitialized pointers, memory leak, and style

* Set CG tolerance units in Python API

* Test ConstantPotentialForce serialization

* Read/write ExceptionsUsePeriodicBoundaryConditions as bool

* Improve constrained conjugate gradient robustness to roundoff error accumulation

* Recompute matrix if electrode atoms move due to setPositions()

* Tolerance is now in gradient (potential) units again

* Add neutralizing background correction

* Add Python API tests

* Fixes for CG and nonbonded exceptions

* Add initial tests checking against existing NonbondedForce behavior

* Expand test suite and fix some implementation issues

* Add additional tests using larger reference system

* Add Gaussian test

* Finish test against reference computation

* CPU platform implementation

* Fixes for compilation on some platforms

* Fixes for constant potential with AVX/AVX2

* Test linking CPU PME library to constant potential test directly

* Older SWIG versions don't support Python set to C++ set conversion

* Add user guide entry

* Increase speed of reference test

* Conditional building constant potential CPU test is unreliable

* Debugging

* Miscellaneous fixes and improvements for CI

* Cache charges so solver will not run if system and coordinates have not changed

* Preconditioner flag, stability, and automatic detection improvements

* Add GPU platform-specific constant potential kernel classes

* PME and device-host I/O changes to support constant potential

* Initial common constant potential implementation

* Constant potential fixes:

* Fix preconditioner PME position/charge save/restore logic

* Fix reduction synchronization in constant potential solver kernels

* Add double-float accumulation for conjugate gradient solver when
  double unsupported by hardware

* Improve conditioning of a test system, and make sure particles are in or
out of cutoff for consistency and ease of comparing between platforms

* Reorder guess charges for CG when atom reordering changes positions

* Remove PME queue for now

* Trying to debug optimized direct space derivative kernel

* Remove extraneous debugging lines

* Style updates; just make CPU preconditioner double precision

* Debugging updated optimized direct derivatives kernel for all but OpenCL CPU

* OpenCL CPU implementation of direct space derivatives, and cleanup

* Try to make test even shorter to not time out on CI

* Temporary - Debugging

* Debugging

* Debugging

* Debugging

* Debugging

* Remove debugging code and fix reduction synchronization

* Fix other reductions

* Debugging - are tests hanging or just slow on CI?

* Debugging

* Debugging

* Fix macro for case when double precision is available on hardware

* Remove changes for debugging again

* Try to improve matrix solver cache locality by uploading transpose

* Fixes for atom ordering and periodic images

* Can't rely on reorder listener for cell offset updates

* Test reducing number of contexts and timing for CI

* Debugging

* Remove timing code and revert debugging changes

* Matrix solver and plasma term optimizations

* Reduce CG solver kernel calls and downloads

* Don't read back convergence flag from global memory

* Update PME due to refactoring in master branch

* Faster matrix solver (1st step)

* Faster matrix solver for CUDA

* Faster matrix solver compatibility with non-CUDA platforms

* Matrix solver fixes

* Use warp shuffle reductions when possible

* Attempt to work around intermittent compiler crash in Intel CPU OpenCL

* Optimize CG solver kernel 1

* Rework CG solver so some kernels can use more than 1 block

* Don't run out of shared memory

* Asynchronously download convergence flag while clearing buffers

---------
Co-authored-by: Evan Pretti <pretti@sh03-17n15.int>

* Use fixed point spread charge on RDNA4 as it is faster

Even though RDNA4 (gfx12) has global_atomic_add_f32, micro-benchmarks and OpenMM benchmarks show
that it is very slow compared to global_atomic_add_u64.

* Add a workaround for fixed point gridSpreadCharge on RDNA4

Workaround for rare cases when few values of pmeGrid are very large and
incorrect. The cause is unknown. Why this workaround or other irrelevant
changes like printf help is also unknown.

* Optimized setPositions() and setVelocities()

* Fix test failures

* Use common API for kernels

* More code uses common interface

* Bug fixes

* Unified interface for sorting

* Simplified interface for FFT

* Use common event API for synchronization

* Minor changes to make code more consistent between platforms

* Common implementation of NonbondedForce

* Bug fixes

* Flag to enable list of single pairs

* CUDA and OpenCL use common implementation of NonbondedForce

* Fixed compilation error

* HIP uses common implementation of NonbondedForce

* Unified interface for queues

* Simplified stream handling in CudaFFT3D

* HIP implementation of ComputeQueue

* Unified interface for FFTs

* AMOEBA uses unified interface for FFTs

* HIP implementation of common FFT interface

* Unified lots of parallel computation code between platforms

* Unified test code between platforms

* Eliminated duplicated timing code

CustomCPPForceImpl for writing forces in C++

    https://github.com/openmm/openmm/commit/9a0db72
    https://github.com/openmm/openmm/pull/4231

Virtual sites can depend on other virtual sites

    https://github.com/openmm/openmm/commit/71f4b3f

Use LF-Middle for LangevinIntegrator and VariableLangevinIntegrator

    https://github.com/openmm/openmm/commit/86988b9

Merged more code into common platform

    https://github.com/openmm/openmm/commit/5739788

    * Common implementation of BondedUtilities
    * Common implementation of UpdateStateDataKernel

Fixed periodic box changing from rectangular to triclinic

    https://github.com/openmm/openmm/commit/75d4f29

Use a small kernel for copying interactionCounts to host memory

    hipMemcpy's CopyDeviceToHost operation has higher latency.

Do not set stream and event blocking/spin related flags

    Let the runtime choose the best option because overriding does not
    improve performance in most cases.

Remove NULL streams and use nonblocking streams explicitly

Make HipContext::pushAsCurrent/popAsCurrent thread-safe as they can be
called simultaneously from different threads via ContextSelector.

Allow peer access to be enabled more than once (if there are multiple
simulations one after another, like in benchmark.py).

Create peerCopyStream on a corresponding device

Use two-speed load balancing for multi GPU runs

    First 100 steps do coarse balancing, next 100 - fine tuning.
    Also ignore the slowest device (usually 0) if its fraction has
    reached 0, (i.e. no work can be transfered to other devices) and
    balance other devices.

Do not download inteactionCounts in parallel nonbonded tasks

    This is not required because updateNeighborListSize has been called
    and valid flag changed.

Initialize tilesAfterReorder properly

    It may contain a garbage value, and if it is large then
    updateNeighborListSize does not force reorder atoms after 25 steps
    in extremal cases.

Use cuCtxPushCurrent() and cuCtxPopCurrent() for selecting CUDA context

    https://github.com/openmm/openmm/pull/3258

Fixed uninitialized memory access

    https://github.com/openmm/openmm/issues/3392
    https://github.com/openmm/openmm/pull/3399

Fixed potential invalid memory access

    See https://github.com/openmm/openmm/pull/3428

Improved temperature reporting for Drude particles

    https://github.com/openmm/openmm/pull/3486
    https://github.com/openmm/openmm/commit/a5e42f5

Fixed race condition with multiple GPUs

    https://github.com/openmm/openmm/commit/6fb1c8a41edff980862750bc086f6a204eb50941

Use blocking sync when creating events

    https://github.com/openmm/openmm/commit/fe21d5ee4f14673a4ea38b7244991772a64ceec2

Very minor optimizations

    https://github.com/openmm/openmm/commit/109f6b2535da4e0c0dd88007d6ca06b4add2ce81

Use PocketFFT

    https://github.com/openmm/openmm/commit/1dac981a63300a2a53a7925f570995914f7163ed

Improved logic for deciding when to reorder atoms

    https://github.com/openmm/openmm/commit/48664a1f1a4490a4dabc277757545ac070e7b898

Ensure valid atom order after loading a checkpoint

    https://github.com/openmm/openmm/commit/a056d5a3754e193105409afa12c9f0c9a2d972a2

Improve performance running on multiple GPUs

    https://github.com/openmm/openmm/commit/0c82c2647de98da5c6dab7bf7a7b8b19705aadc0

Fixed errors when running on multiple GPUs

    https://github.com/openmm/openmm/commit/ed9df876d43c037c08d4762721e73e5caae086d9

Optimized reducing energy

    https://github.com/openmm/openmm/commit/2975f44

* VkFFT-based 3D FFT;
* Caching of compiled VkFFT kernels;
* Extend FFT tests with more sizes.

* Unload all loaded modules in HipContext's destructor,
  HIP modules keep file desctriptors opened, but OpenMM never unloads
  modules leaking these file descriptors. This can cause crashinf of
  some scripts like test-openmm-platforms from openmmtools.
* ROCm 6.0 defines operator* for complex types (that are typedefs for
  float2 and double2), they conflict with operators defined for vectors.
  This is fixed in newer ROCm versions.
* Revert HIP_DYNAMIC_SHARED back to extern __shared__ (the macro is
  in the headers).
* Reduce the speed of the HIP platform if there are no HIP devices in
  the system.

* Compile with -munsafe-fp-atomics to enable fast hardware f32 atomic
  add on global memory on pre-MI100 GPUs;
* Use fixed point charge spreading on other GPUs, otherwise float atomic
  add will be compiled as a slow CAS loop;
* Tune block sizes, use executeKernelFlat;
* Tune launch bounds of PME grid-related kernels: force the compiler to
  use all registers by limiting max waves per EU to 1.

* All AMD GPUs support shuffle, double precision and 64-bit int atomics;
* Remove unused code: !ENABLE_SHUFFLE code paths in nonbonded.hip;
* Use intrinsics in single-precision;
* Use realToFixedPoint (faster float32-to-int64);
* Remove shared atomIndices, use shuffles;
* Check early if atoms are in the cutoff range, sometimes all lanes in
  a warp can skip computations, single pairs can also skip useless
  atomics with zero values;
* Remove volatile skipTiles access, use shuffles;
* Distribute work for warps in a strided order;
* Skip warps that may be still busy in the first loop;
* Unify conditions for excluded atoms with `includeInteraction`;
* Move multiprocessors to HipContext;
* Increase number of warps for computeNonbonded;
* Disable packed math for >=MI200 (it affects performance of some
  kernels like computeGKForces of amoebagk);
* Remove defaultOptimizationOptions and createModule's optimizationFlags
  as they are never used;
* Support -save-temps.

* Compile kernels with max block size of 256 threads:
  The default hipcc behavior since ROCm 4.2 is to compile kernels
  with 1024 threads unless __launch_bounds__ is specified. This
  significantly increases register pressure especially in heavy kernels
  (double precision, for example), requiring register spilling;
* Optimize computeRange by using multiple blocks for reduction;
* Use blocks of 1024 threads for computeBucketPositions - it is executed
  as a single work group so larger block size is faster;
* Sort up-to lenghtNextPow2 instead of blockDim.x (faster for short
  buckets);
* Optimize sortShortList2;
* Optimize sortBuckets with bit instructions;
* Decrease bucket size for non-uniform sorting: too many buckets may
  have sizes too large to sort in shared memory;
* Add more sizes in tests.

Port changes in CUDA backend to HIP

Fix a warning about arithmetic operations on void* in HipArray::uploadSubArray

Fix "Error Initializing context ROCm 5.3.0"

    https://github.com/StreamHPC/openmm-hip/issues/3


    hipDeviceSetCacheConfig returns hipErrorNotSupported on 5.3
Co-authored-by: Nick Curtis <nicholas.curtis@amd.com>

* Common implementation of BondedUtilities

* Common implementation of UpdateStateDataKernel

* Always use nvrtc for compilation

* Install nvrtc on CI

* Workaround for compiler error

* Set empty values for deprecated properties

* Use cuCtxPushCurrent() and cuCtxPopCurrent() for selecting CUDA context

* Fixed errors in amoeba coda

* Fixed more errors in context selection

* Began converting AMOEBA to common platform

* Beginning of OpenCL platform for AMOEBA

* Converted AmoebaVdwForce to common platform

* Cleaned up reference AMOEBA tests

* Began converting AmoebaMultipoleForce to common platform

* Continue converting AmoebaMultipoleForce to common platform

* Bug fixes

* Bug fix

* Continue converting AmoebaMultipoleForce to common platform

* Converting AmoebaMultipoleForce and AmoebaGeneralizedKirkwoodForce to common platform

* Converting AmoebaMultipoleForce and AmoebaGeneralizedKirkwoodForce to common platform

* Creating OpenCL version of AmoebaMultipoleForce and AmoebaGeneralizedKirkwoodForce

* Creating OpenCL version of AmoebaMultipoleForce and AmoebaGeneralizedKirkwoodForce

* Creating OpenCL version of AmoebaMultipoleForce and AmoebaGeneralizedKirkwoodForce

* Converted arrays from real3 to real

* Bug fix to OpenCL AmoebaGeneralizedKirkwoodForce

* Fixes for AMD GPUs

* Began converting HippoNonbondedForce to common platform

* Continuing to convert HippoNonbondedForce to common platform

* Continuing to convert HippoNonbondedForce to common platform

* Working on unifying PME kernels

* Fixed error on devices without 64 bit atomics

* Unified PME kernels

* Converted HippoNonbondedForce to common platform

* Creating OpenCL implementation of HippoNonbondedForce

* Continuing OpenCL implementation of HippoNonbondedForce

* Mostly finished OpenCL implementation of HippoNonbondedForce

* Eliminated three component vector types in host code

* Fix errors on CPU OpenCL

* Skip double precision tests for AMOEBA on OpenCL

* Bug fixes

* Bug fixes

* Fixed compilation error

Co-authored-by: David Clark <daclark@nvidia.com>

* Began implementing geometric functions on points

* Started common implementation of point functions

* Completed implementation of point functions for CustomCompoundBondForce

* Implemented point functions for CustomCentroidBondForce

* Implemented point functions for CustomManyParticleForce

* Use point functions to simplify implementation of custom forces

* Removed unnecessary code

* Fixed typo

* Improve selection of architecture to compile for

* Fixed compilation errors on older CUDA versions

* Use command line compiler if it is explicitly specified

* Don't set OPENMM_CUDA_COMPILER on Jenkins

* Began creating common compute framework to unify code between CUDA and OpenCL

* Began OpenCL implementation of common compute framework

* Common implementation of CMMotionRemover

* CUDA implementation of common compute interface

* Converted HarmonicBondForce to common compute API

* Converted standard bonded forces to common compute API

* Converted ExpressionUtilities to common compute API

* Created ComputeParameterSet

* Converted custom bonded forces to common compute API

* Converted CustomCentroidBondForce to common compute API

* Converted CustomManyParticleForce to common compute API

* Moved lots of duplicate code from CudaContext and OpenCLContext to ComputeContext

* Converted GayBerneForce to common compute API

* Removed obsolete kernels

* Converted verlet integrators to common compute API

* Converted Langevin and Brownian integrators to common compute API

* Converted CustomIntegrator to common compute API

* Converted CustomNonbondedForce to common compute API

* Removed uses of a deprecated API

* Fixed failing test cases

* Converted GBSAOBCForce to common compute API

* Began converting CustomGBForce to common compute API

* Finished converting CustomGBForce to common compute API

* Merged duplicated code in CudaIntegrationUtilities and OpenCLIntegrationUtilities

* Converted RMSDForce and AndersenThermostat to common compute API

* Converted CustomHbondForce to common compute API

* Merged scripts for encoding kernel sources

* Converted Drude plugin to common compute API

* Fixed errors in CMake scripts

* Attempt at fixing errors on Windows

* Added discussion of common compute API to developer guide

* Added Windows export macro for common classes

* Fixed error in CMMotionRemover

* Ubdated travis to newer Ubuntu version

* Fixed errors on CPU OpenCL

* Fixed Windows linking errors

* Added missing pragma for 32 bit atomics

* Replaced long long with mm_long

* More fixes to Windows linking

* Bug fix