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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

* 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.

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>

* Reference platform supports nested virtual sites

* Common platform supports nested virtual sites

* Fixed force distribution from nested virtual sites

* Fixed test failures

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

* Fixed errors in amoeba coda

* Fixed more errors in context selection

* CCMA with a small number of constraints uses a single kernel

* Fixed compilation errors in kernel

* Fixed compilation errors in kernel

* Further optimizations to CCMA with few constraints

* 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

Context into the platform rather than hacked into getRandomNumberSeed...

Someone please check this.

When computing kinetic energy, make sure the shifted velocities are perpendicular to the constraints

Reduced memory use for random numbers, eliminated unnecessary calculation when generating random numbers

No commit message

When converting to fixed point, multiply by 0x100000000 instead of 0xFFFFFFFF.  This should be (very very slightly) more accurate, since its reciprocal can be exactly represented in floating point.

Began implementing new mixed precision model that does integration in double precision and force evaluation in single precision

Continuing to implement new CUDA platform: constraints, LangevinIntegrator, BrownianIntegrator, VariableLangevinIntegrator, VariableVerletIntegrator