GitLab

* API for querying devices

* CUDA and HIP implementations of getDevices()

* Fix test failures

* Fix test failures

* CUDA returns correct devices even if no context has been created

* Return a single device for Reference and CPU

* Fix CI failure

* 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(...

* Unified storage of global parameters

* Fixes to CUDA and HIP

* Store global parameters as real instead of float

* 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

* Add correction for self energy of neutralizing plasma

* Fixed compilation errors

* Update total charge in copyParametersToContext()

* Bug fixes

* Fixed compilation errors in HIP

* Bug fix

* Created DPDIntegrator class

* Reference implementation of DPDIntegrator

* Build neighbor list for DPDIntegrator

* Minor fixes

* Documentation for DPDIntegrator

* Python API for DPDIntegrator

* Preliminary OpenCL implementation of DPDIntegrator

* Enable USE_PERIODIC

* Use updated positions in DPD thermostat

* Working on neighbor list for OpenCL DPDIntegrator

* ReorderListener for particle types

* Serialization for DPDIntegrator

* CUDA implementation of DPDIntegrator

* HIP implementation of DPDIntegrator

* Fixed compile error in Python wrapper

* Fixed compile error in wrappers

* Fixed uninitialized memory in reference neighbor list

* Added DPDIntegrator to C++ API docs

* Fixed incorrect launch size

* Fixed nan in DPD random number generator

* Minor optimizations

* Improved load balancing

* Fixed an indexing error

* Neighbor list uses the maximum cutoff of any force

* Fixed HIP compilation error

* Fixed access to invalid memory

* Added test case for diffusion...

* Replace pthreads with C++ threads

* Try to fix CI errors

* Try including -pthread linker option

* updateParametersInContext() can modify parameter offsets

* Reordering respects parameter offsets

* Implemented for CUDA and HIP

* PME_ORDER threads process one atom;
* PME_ORDER threads access consecutive addresses;
* No need to permute z indices with zindexTable;
* finishSpreadCharge is needed only with fixed point charge spreading;

* Unified lots of parallel computation code between platforms

* Unified test code between platforms

* Eliminated duplicated timing code

* Optimize CustomNonbondedForce.updateParametersInContext()

* Optimized uploading changed values to GPU

* Optimized updateParametersInContext() for lots of bonded forces

* Optimized updateParametersInContext() for CustomExternalForce

* Optimized updateParametersInContext() for NonbondedForce

* Code changes for HIP platform

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

Skip neighbor list for very small systems

    https://github.com/openmm/openmm/pull/4070

Store bounding box sizes in half precision

    https://github.com/openmm/openmm/commit/2ae50f9

Use large blocks to optimize building the neighbor list

    https://github.com/openmm/openmm/commit/3955033

Improved sorting of blocks when building neighbor list

    https://github.com/openmm/openmm/commit/796ffaa

Fixed bug in large blocks optimization with triclinic boxes

    https://github.com/openmm/openmm/commit/4c10732

Optimize sorting of non-uniformly distributed data

    https://github.com/openmm/openmm/commit/71d9bb1

Co-authored-by: bdenhollander <44237618+bdenhollander@users.noreply.github.com>

Co-authored-by: Emilio Gallicchio <emilio.gallicchio@gmail.com>

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.

Optimize findBlocksWithInteractions

* Replace volatile shared mem accesses with shuffles;
* Add NUM_TILES_IN_BATCH for processing block1 by multiple warps
  (for small systems);
* Cherry-pick missing changes from .cu;
* Tune MAX_BITS_FOR_PAIRS depending on device and the system size;
* Store single pairs immediately (if there are any), this allows not to
  store flags to shared memory and filter buffer and flagsBuffer after
  saving single pairs;
* Use fma explicitly and sign bit for better device code;
* Use CDNA's MFMA with singe/mixed precision;
* On CDNA the coarse grained stage processes warpSize blocks for
  one block1, the fine grained stage checks atoms of two block2 vs atoms
  of the same block1, singlePairs and interactingAtoms are also stored
  by warps, not half-warps;

Optimize findBlockBounds

* Use shuffles;
* Use executeKernelFlat;
* Process 2 tiles per warp 64 on CDNA;
* Use more uniformly distributed keys when sorting blocks;

Use compareInt2LargeSIMD when tile size < SIMD width

Fix exclusion tiles sorting on AMD CDNA (64 threads per wave)

    The nonbonded kernel uses USE_NEIGHBOR_LIST (useNeighborList)
    so host code also must check it instead of useCutoff.

    See also https://github.com/openmm/openmm/issues/3462

* 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>