FLAME GPU 2.0.0-rc.2

FLAME GPU 2.0.0-rc.2 is the third release-candidate for FLAME GPU 2.0.0

As a release-candidate the API should be stable, unless any issues are found during the release-candidate phase which require a breaking change to resolve.

There are no breaking API changes since 2.0.0-rc.1, however, there are several deprecations:

• Python binary wheels from the next release will require glibc >= 2.28 (installation from source with older glibc should still be functional, but no longer tested)
• Python 3.8 binary wheels will not be produced from the next release due to EoL
• Support for CUDA 11.0 and CUDA 11.1 will be dropped from a future release due to nvidia/CCCL support (#1155)

See the changelog for more detail.

This release-candidate release requires:

• CMake >= 3.18
• CUDA >= 11.0 and a Compute Capability >= 3.5 NVIDIA GPU.
• C++17 capable C++ compiler (host), compatible with the installed CUDA version (i.e VS2019+, or GCC >= 8.1)
• git
• Python >= 3.8 (optional)
• MPI >= 3 (optional) with CMake >= 3.20

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website
• Pyflamegpu Wheelhouse

Installing Pre-compiled Python Binary Wheels

Python binary wheels for pyflamegpu are not currently distributed via pip, however, they can be installed from the pyflamegpu wheelhouse - whl.flamegpu.com.
They can also be installed manually by downloading assets from this wheel. the previous be installed manually from assets attached to this release.

To install pyflamegpu 2.0.0rc2 from whl.flamegpu.com, install via pip with --extra-index-url or --find-links and the appropriate URI from whl.flamegpu.com.
E.g. to install the latest pyflamegpu build for CUDA 11.2-11.8 without visualiastion:

python3 -m pip install --extra-index-url https://whl.flamegpu.com/whl/cuda112/ pyflamegpu

To install pyflamegpu 2.0.0rc2 manually, download the appropriate .whl file for your platform, and install it into your python environment using pip. I.e.

python3 -m pip install pyflamegpu-2.0.0rc2+cuda112-cp39-cp39-linux_x86_64.whl

CUDA 11.2-11.8 or CUDA 12.x including nvrtc must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU.

Python binary wheels are available for x86_64 systems with:

• Linux with glibc >= 2.17 (I.e. Ubuntu >= 13.04, CentOS/RHEL >= 7+, etc.)
• Windows 10+
• Python 3.8 - 3.12
• CUDA 12.x
  • Built with support for Compute Capability 50 60 70 80 90 GPUs
• CUDA 11.2 - 11.8
  • Built with support for Compute Capability 35 50 60 70 80 GPUs
• Wheels with visualisation enabled or disabled.
  • Note that Linux wheels do not package shared object dependencies at this time, so are not strictly ManyLinux compliant

Wheel filenames are of the format pyflamegpu-2.0.0rc2+cuda<CUDA>[.vis]-cp<PYTHON>-cp<PYTHON>-<platform>.whl, where:

• cuda<CUDA> encodes the CUDA version used
• .vis indicates visualisation support is included
• cp<PYTHON> identifies the python version
• <platform> identifies the OS/CPU Architecture

For Example:

• pyflamegpu-2.0.0rc2+cuda120-cp38-cp38-linux_x86_64.whl is a CUDA 12.0-12.x compatible wheel, without visualisation support, for python 3.8 on Linux x86_64.
• pyflamegpu-2.0.0rc2+cuda112.vis-cp39-cp39-win_amd64.whl is a CUDA 11.2 - 11.8 compatible wheel, with visualisation support, for python 3.9 on Windows 64-bit.

Building FLAME GPU from Source

For instructions on building FLAME GPU from source, please see the Building FLAME GPU section of the README.

Known Issues

• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.
• CUDA 12.2+ suffers from poor RTC compilation times, to be fixed in a future release. (#1118)
• Wrapped spatial message iteration with may incorrectly report that the radius is not a factor of the environment with FLAMEGPU_SEATBELTS=ON for certain floating point values, to be fixed in a future release (#1177).
• Windows Driver 460 may encounter invalid argument errors when embedded PTX is used to execute on a higher compute capability device. Upgrading to 461 (CUDA 12.6 Update 3) or ensuring you compile with the correct CMAKE_CUDA_ARCHITECTURES appears to resolve this issue. See #1253 for more information.

FLAME GPU 2.0.0-rc.1

FLAME GPU 2.0.0-rc.1 is the second release-candidate for FLAME GPU 2.0.0

As a release-candidate the API should be stable, unless any issues are found during the release-candidate phase which require a breaking change to resolve.

There are several important additions and a breaking change since FLAME GPU 2.0.0-rc, which may require changes to your models.
See the changelog for more detail.

This release-candidate release requires:

• CMake >= 3.18
• CUDA >= 11.0 and a Compute Capability >= 3.5 NVIDIA GPU.
• C++17 capable C++ compiler (host), compatible with the installed CUDA version (i.e VS2019+, or GCC >= 8.1)
• git
• Python >= 3.8 (optional)
• MPI >= 3 (optional) with CMake >= 3.20

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website
• Pyflamegpu Wheelhouse

Installing Pre-compiled Python Binary Wheels

Python binary wheels for pyflamegpu are not currently distributed via pip, however, they can now be installed from the pyflamegpu wheelhouse - whl.flamegpu.com.
They can also be installed manually by downloading assets from this wheel. the previous be installed manually from assets attached to this release.

To install pyflamegpu 2.0.0rc1 from whl.flamegpu.com, install via pip with --extra-index-url or --find-links and the appropriate URI from whl.flamegpu.com.
E.g. to install the latest pyflamegpu build for CUDA 11.2-11.8 without visualiastion:

python3 -m pip install --extra-index-url https://whl.flamegpu.com/whl/cuda112/ pyflamegpu

To install pyflamegpu 2.0.0rc1 manually, download the appropriate .whl file for your platform, and install it into your python environment using pip. I.e.

python3 -m pip install pyflamegpu-2.0.0rc0+cuda112-cp39-cp39-linux_x86_64.whl

CUDA 11.2-11.8 or CUDA 12.x including nvrtc must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU.

Python binary wheels are available for x86_64 systems with:

• Linux with glibc >= 2.17 (I.e. Ubuntu >= 13.04, CentOS/RHEL >= 7+, etc.)
• Windows 10+
• Python 3.8 - 3.12
• CUDA 12.x
  • Built with support for Compute Capability 50 60 70 80 90 GPUs
• CUDA 11.2 - 11.8
  • Built with support for Compute Capability 35 50 60 70 80 GPUs
• Wheels with visualisation enabled or disabled.
  • Note that Linux wheels do not package shared object dependencies at this time.

Wheel filenames are of the format pyflamegpu-2.0.0rc1+cuda<CUDA>[.vis]-cp<PYTHON>-cp<PYTHON>-<platform>.whl, where:

• cuda<CUDA> encodes the CUDA version used
• .vis indicates visualisation support is included
• cp<PYTHON> identifies the python version
• <platform> identifies the OS/CPU Architecture

For Example:

• pyflamegpu-2.0.0rc1+cuda120-cp38-cp38-linux_x86_64.whl is a CUDA 12.0-12.x compatible wheel, without visualisation support, for python 3.8 on Linux x86_64.
• pyflamegpu-2.0.0rc1+cuda112.vis-cp39-cp39-win_amd64.whl is a CUDA 11.2 - 11.8 compatible wheel, with visualisation support, for python 3.9 on Windows 64-bit.

Building FLAME GPU from Source

For instructions on building FLAME GPU from source, please see the Building FLAME GPU section of the README.

Known Issues

• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.
• CUDA 12.2+ suffers from poor RTC compilation times, to be fixed in a future release. (#1118).

Breaking changes compared to 2.0.0-rc

• CUDAEnsemble::getLogs returns std::map<unsigned int, RunLog> rather than std::vector<RunLog>, required for distributed ensemble support. (#1090)

FLAME GPU 2.0.0-rc

FLAME GPU 2.0.0-rc is the first release-candidate for FLAME GPU 2.0.0

As a release-candidate the API should be stable, unless any issues are found during the release-candidate phase which require a breaking change to resolve.

There are many significant changes since FLAME GPU 2.0.0-alpha.2, which will require changes to CMakeLists.txt and to models. See the changelog for more detail.

This release-candidate release requires:

• CMake >= 3.18
• CUDA >= 11.0 and a Compute Capability >= 3.5 NVIDIA GPU.
• C++17 capable C++ compiler (host), compatible with the installed CUDA version (i.e VS2019+, or GCC >= 8.1)
• git
• Python >= 3.7 (optional)

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website

Installing Pre-compiled Python Binary Wheels

To simplify use of the python binaries, Python wheels are offered with this release.
These are not yet available through a distribution channel.

To install pyflamegpu 2.0.0rc, download the appropriate .whl file for your platform, and install it into your python environment using pip. I.e.

python3 -m pip install pyflamegpu-2.0.0rc0+cuda112-cp39-cp39-linux_x86_64.whl

CUDA 11.0 or CUDA 11.2 (or newer) including nvrtc must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU.

Python binary wheels are available for x86_64 systems with:

• Linux with glibc >= 2.17 (I.e. Ubuntu >= 13.04, CentOS/RHEL >= 7+, etc.)
• Windows 10+
• Python 3.7 - 3.11
• CUDA 11.0 or 11.2 (for 11.2-11.8)
  • Built with support for Compute Capabilities 35 50 60 70 80
  • CUDA 12.x compatible wheels will be provided by a future release
• Wheels with visualisation enabled or disabled.
  • Note that Linux wheels do not package shared object dependencies at this time.

Wheel filenames are of the format pyflamegpu-2.0.0rc0+cuda<CUDA>[.vis]-cp<PYTHON>-cp<PYTHON>-<platform>.whl, where:

• cuda<CUDA> encodes the CUDA version used
• .vis indicates visualisation support is included
• cp<PYTHON> identifies the python version
• <platform> identifies the OS/CPU Architecture

For Example:

• pyflamegpu-2.0.0rc0+cuda110-cp38-cp38-linux_x86_64.whl is a CUDA 11.0 compatible wheel, without visualisation support, for python 3.8 on Linux x86_64.
• pyflamegpu-2.0.0rc0+cuda112.vis-cp39-cp39-win_amd64.whl is a CUDA 11.2+ compatible wheel, with visualisation support, for python 3.9 on Windows 64-bit.

Building FLAME GPU from Source

For instructions on building FLAME GPU from source, please see the Building FLAME GPU section of the README.

Known Issues

• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.

FLAME GPU 2.0.0-alpha.2

FLAME GPU 2.0.0-alpha.2 is the second alpha pre-release of FLAME GPU 2.0.0.

As an alpha release, the API cannot be considered stable, as there will likely be breaking changes compared to the first stable 2.0.0 release, however, we aim to minimise the number of such changes.

FLAME GPU 2 is a complete rewrite of FLAME GPU 1, using modern templated CUDA C++ with CUDA C++ and Python interfaces available (requires NVRTC).

This alpha release requires:

• CUDA >= 10.0 including nvrtc and a CUDA-capable NVIDIA GPU (Compute Capability >= 35)
• A c++14 host compiler compatible with your CUDA installation, I.e. GCC or MSVC depending on platform
• CMake >= 3.18
• git

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website

Installing Pre-compiled Python Binary Wheels

To simplify use of the python binaries, Python wheels are offered with this release.
These are not yet available through a distribution channel.

To install pyflamegpu 2.0.0a2, download the appropriate .whl file for your platform, and install it into your python environment using pip. I.e.

python3 -m pip install --user pyflamegpu-2.0.0a2+cuda112-cp39-cp39-linux_x86_64.whl

CUDA 11.0 or CUDA 11.2 (or newer) including nvrtc must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU.

Python binary wheels are available for x86_64 systems with:

• Linux with glibc >= 2.17 (I.e. Ubuntu >= 13.04, CentOS/RHEL >= 7+, etc.)
• Windows 10
• Python 3.6, 3.7, 3.8 and 3.9
• CUDA 11.0 or 11.2+
  • Built with support for Compute Capabilities 35 52 60 70 80
• Wheels with visualisation enabled or disabled.
  • Note that Linux wheels do not package shared object dependencies at this time.

Wheel filenames are of the format pyflamegpu[_vis]-2.0.0a2+cuda<CUDA>-cp<PYTHON>-cp<PYTHON>-<platform>.whl, where:

• _vis indicates visualisation support is included
• +cuda<CUDA> encodes the CUDA version used
• cp<PYTHON> identifies the python version
• <platform> identifies the OS/CPU Architecture

For Example

• pyflamegpu-2.0.0a2+cuda110-cp38-cp38-linux_x86_64.whl is a non-visualisation build, built with CUDA 11.0, for python 3.8 on Linux x86_64.
• pyflamegpu_vis-2.0.0a2+cuda112-cp39-cp39-win_amd64.whl is a visualisation-enabled build, built with CUDA 11.2, for python 3.9 on Windows 64-bit.

Building FLAME GPU from Source

For instructions on building FLAME GPU from source, please see the Building FLAME GPU section of the README.

Deprecated Requirement Versions

Several CUDA and C++ compilers versions currently work but are marked as deprecated, with support to be removed in a future pre-release version of FLAME GPU.

This is due to planned adoption of C++17 features.
C++14 support is deprecated and will be removed in a future release.
This means that the following compiler/software versions are deprecated:

• CUDA >= 10.0 && < 11.0
• C++14 host compilers:
  • GCC >=6 && < 7
  • Visual Studio 2017 may or may not work.

Known Issues

There are known issues with this release of FLAME GPU 2, which will be fixed where possible in future releases.
For a full list of issues please see the Issue Tracker.

• Performance regressions in CUDA 11.3+, due to changes in compiler register usage (#560).
• Segfault when using flamegpu::DependencyGraph via the default constructor (#555). This will require an API break to resolve.
• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA < 11.0 may encounter intermittent compiler failures. Please use CUDA 11.0+.
  • This will be resolved by dropping CUDA 10 support in a future release.
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.
• Original releases of GCC 10.3.0 and 11.1.0 were incompatible with CUDA when using <chrono>, with a fix backported into these versions in some cases (575)
  • CMake will attempt to detect <chrono> support, and prevent compilation if an incompatible version is detected.
• CUDAEnsemble::EnsembleConfig::devices is not programtically acessible from python (#682)
• RunPlan and RunPlanVector operator+/operator+= overloads are not available in python (#679)
  • It is likely that a breaking change will be requied to resolve this.

FLAME GPU 2.0.0-alpha.1

FLAME GPU 2.0.0-alpha.1 is the second alpha pre-release of FLAME GPU 2.0.0.

As an alpha release, the API cannot be considered stable, as there will likely be breaking changes compared to the first stable 2.0.0 release, however, we aim to minimise the number of such changes.

FLAME GPU 2 is a complete rewrite of FLAME GPU 1, using modern templated CUDA C++ with CUDA C++ and Python interfaces available (requires NVRTC).

This alpha release requires:

• CUDA >= 10.0 including nvrtc and a CUDA-capable NVIDIA GPU (Compute Capability >= 35)
• A c++14 host compiler compatible with your CUDA installation, I.e. GCC or MSVC depending on platform
• CMake >= 3.18
• git

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website

Installing Pre-compiled Python Binary Wheels

To simplify use of the python binaries, Python wheels are offered with this release.
These are not yet available through a distribution channel.

To install pyflamegpu 2.0.0a0, download the appropriate .whl file for your platform, and install it into your python environment using pip. I.e.

python3 -m pip install --user pyflamegpu-2.0.0a1+cuda112-cp39-cp39-linux_x86_64.whl

CUDA 11.0 or CUDA 11.2 (or newer) including nvrtc must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU.

Python binary wheels are available for x86_64 systems with:

• Linux with glibc >= 2.17 (I.e. Ubuntu >= 13.04, CentOS/RHEL >= 7+, etc.)
• Windows 10
• Python 3.6, 3.7, 3.8 and 3.9
• CUDA 11.0 or 11.2+
  • Built with support for Compute Capabilities 35 52 60 70 80
• Wheels with visualisation enabled or disabled.
  • Note that Linux wheels do not package shared object dependencies at this time.

Wheel filenames are of the format pyflamegpu[_vis]-2.0.0a1+cuda<CUDA>-cp<PYTHON>-cp<PYTHON>-<platform>.whl, where:

• _vis indicates visualisation support is included
• +cuda<CUDA> encodes the CUDA version used
• cp<PYTHON> identifies the python version
• <platform> identifies the OS/CPU Architecture

For Example

• pyflamegpu-2.0.0a1+cuda110-cp38-cp38-linux_x86_64.whl is a non-visualisation build, built with CUDA 11.0, for python 3.8 on Linux x86_64.
• pyflamegpu_vis-2.0.0a1+cuda112-cp39-cp39-win_amd64.whl is a visualisation-enabled build, built with CUDA 11.2, for python 3.9 on Windows 64-bit.

Building FLAME GPU from Source

For instructions on building FLAME GPU from source, please see the Building FLAME GPU section of the README.

Deprecated Requirement Versions

Several CUDA and C++ compilers versions currently work but are marked as deprecated, with support to be removed in a future pre-release version of FLAME GPU.

This is due to planned adoption of C++17 features.
C++14 support is deprecated and will be removed in a future release.
This means that the following compiler/software versions are deprecated:

• CUDA >= 10.0 && < 11.0
• C++14 host compilers:
  • GCC >=6 && < 7
  • Visual Studio 2017 may or may not work.

Known Issues

There are known issues with this release of FLAME GPU 2, which will be fixed where possible in future releases.
For a full list of issues please see the Issue Tracker.

• Performance regressions in CUDA 11.3+, due to changes in compiler register usage (#560).
• Segfault when using flamegpu::DependencyGraph via the default constructor (#555). This will require an API break to resolve.
• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA < 11.0 may encounter intermittent compiler failures. Please use CUDA 11.0+.
  • This will be resolved by dropping CUDA 10 support in a future release.
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.
• Original releases of GCC 10.3.0 and 11.1.0 were incompatible with CUDA when using <chrono>, with a fix backported into these versions in some cases (575)
  • CMake will attempt to detect <chrono> support, and prevent compilation if an incompatible version is detected.
• CUDAEnsemble::EnsembleConfig::devices is not programtically acessible from python (#682)
• RunPlan and RunPlanVector operator+/operator+= overloads are not available in python (#679)
  • It is likely that a breaking change will be requied to resolve this.

FLAME GPU 2.0.0-alpha

FLAME GPU 2.0.0-alpha is the first alpha pre-release of FLAME GPU 2.0.0.

As an alpha release, the API cannot be considered stable, as there will likely be breaking changes compared to the first stable 2.0.0 release, however, we aim to minimise the number of such changes.

FLAME GPU 2 is a complete rewrite of FLAME GPU 1, using modern templated CUDA C++ with CUDA C++ and Python interfaces available (requires NVRTC).

This alpha release requires:

• CUDA 10 or greater and a CUDA GPU
• A c++14 host compiler compatible with your CUDA installation, I.e. GCC or MSVC depending on platform
• git

For full version requirements, please see the Requirements section of the README.

Documentation and Support

• Quickstart Guide
• Documentation and User Guide
• GitHub Discussions
• GitHub Issues
• Website

Installing Pre-compiled Python Binary Wheels

Note: These wheels require glibc >= 2.29. as they were build using Ubuntu 20.04's glibc. This prevents use on older Ubuntu's, Debian 9 such as google collab, or CentOS/RHEL 7 or 8. A future release will include wheels with greater compatibility.

To simplify use of the python binaries, Python wheels are offered with this release.
These are not yet available through a distribution channel.

To install pyflamegpu 2.0.0a0, download the appropriate .whl file for your platform, and install it into your python environment using pip.

CUDA 11.0 or newer (including nvrtc) must be installed on your system containing a Compute Capability 3.5 or newer NVIDIA GPU. I.e.

python3 -m pip install --user <filename>.whl

Python binaries are available for python 3.6 to 3.9 for:

• Windows x64, with visualisation enabled: pyflamegpu-2.0.0a0-cpXX-cpXX-win_amd64.whl
• Linux x86_64 with visualisation enabled: pyflamegpu-2.0.0a0-cpXX-cpXX-linux_x86_64.whl
• Linux x86_64 with visualisation disabled: pyflamegpu-console-2.0.0a0-cpXX-cpXX-linux_x86_64.whl
  • Note this installs the python module pyflamegpu not pyflamegpu-console, breaking the wheel filename convention. We intend to address this in a future release.

They were built using CUDA 11.0 for Compute Capabilities 35 52 60 70 80, supporting second generation Kepler GPUs and newer.

Building FLAME GPU from Source

For instructions on building FLAME GPU 2.0.0-alpha from source, please see the Building FLAME GPU section of the README.

Deprecated Requirement Versions

Although this is the first alpha release, several versions of CUDA, CMake and C++ compilers which are currently supported will be unsupported in a future release of FLAME GPU 2.

Currently, FLAME GPU is built as a c++14 or c++17 project depending upon compiler support.
C++14 support is deprecated and will be removed in a future release.
This means that the following compiler/software versions are deprecated:

• CUDA >= 10.0 && < 11.0
• CMake >= 3.15 && < 3.18
• C++14 host compilers:
  • GCC >=6 && < 7
  • Visual Studio 2017 may or may not work.

Known Issues

There are known issues with the 2.0.0-alpha release of FLAME GPU 2, which will be fixed where possible in future releases. For a full list of issues please see the Issue Tracker.

• Performance regressions in CUDA 11.3+, due to changes in compiler register usage (#560).
• Segfault when using flamegpu::DependencyGraph via the default constructor (#555). This will require an API break to resolve.
• Warnings and a loss of performance due to hash collisions in device code (#356)
• Multiple known areas where performance can be improved (e.g. #449, #402)
• Windows/MSVC builds using CUDA < 11.0 may encounter intermittent compiler failures. Please use CUDA 11.0+.
  • This will be resolved by dropping CUDA 10 support in a future release.
• Windows/MSVC builds using CUDA 11.0 may encounter errors when performing incremental builds if the static library has been recompiled. If this presents itself, re-save any .cu file in your executable producing project and re-trigger the build.
• Debug builds under linux with CUDA 11.0 may encounter cuda errors during validateIDCollisions. Consider using an alternate CUDA version if this is required (#569).
• CUDA 11.0 with GCC 9 may encounter a segmentation fault during compilation of the test suite. Consider using GCC 8 with CUDA 11.0.
• CMake 3.16 has known issues on some platforms. CMake versions less than 3.18 are deprecated and support will be removed in a future release.