QUDA 0.9.0

Overview

QUDA is a library for performing calculations in lattice QCD on graphics processing units (GPUs), leveraging NVIDIA's CUDA platform. The current release includes optimized Dirac operators and solvers for the following fermion actions:

• Wilson
• Clover-improved Wilson
• Twisted mass (including non-degenerate pairs)
• Twisted mass with a clover term
• Staggered fermions
• Improved staggered (asqtad or HISQ)
• Domain wall (4-d or 5-d preconditioned)
• Mobius fermion

Implementations of CG, multi-shift CG, BiCGStab, BiCGStab(l), and DD-preconditioned GCR are provided, including robust mixed-precision variants supporting combinations of double, single, and half (16-bit "block floating point") precision. The library also includes auxilliary routines necessary for Hybrid Monte Carlo, such as HISQ link fattening, force terms and clover- field construction. Use of many GPUs in parallel is supported throughout, with communication handled by QMP or MPI. Support for eigen-vector deflation solvers is also included (https://github.com/lattice/quda/wiki/Deflated-Solvers).

We note that while this release of QUDA includes an initial implementation of adaptive multigrid, this is considered experimental and is undergoing continued evolution and improvement. We highly recommend that those users interested in using adaptive multigrid use the present multigrid development branch "feature/multigrid". More details can be found at https://github.com/lattice/quda/wiki/Multigrid-Solver.

Software Compatibility:

The library has been tested under Linux (CentOS 6 and Ubuntu 16.04) using releases 7.5, 8.0 and 9.0, 9.1 and 9.2 of the CUDA toolkit. CUDA 7.0 is not supported, though they may continue to work fine. Eariler versions of the CUDA toolkit will not work. QUDA has been tested in conjuction with both x86-64 and IBM POWER8/POWER9 CPUs. The library also works on 64-bit Intel-based Macs.

See also Known Issues below.

Hardware Compatibility:

For a list of supported devices, see

http://developer.nvidia.com/cuda-gpus

Before building the library, you should determine the "compute capability" of your card, either from NVIDIA's documentation or by running the deviceQuery example in the CUDA SDK, and pass the appropriate value to the QUDA_GPU_ARCH variable in cmake.

QUDA 0.9.0, supports devices of compute capability 2.0 or greater. See also "Known Issues" below.

Installation:

The recommended method for compiling QUDA is to use cmake, and build QUDA in a separate directory from the source directory. For instructions on how to build QUDA using cmake see this page https://github.com/lattice/quda/wiki/Building-QUDA-with-cmake. Note that this requires cmake version 3.1 or later. You can obtain cmake from https://cmake.org/download/. On Linux the binary tar.gz archives unpack into a cmake directory and usually run fine from that directory.

The basic steps for building cmake are:

1. Create a build dir, outside of the quda source directory.
2. In your build-dir run cmake <path-to-quda-src>
3. It is recommended to set options by calling ccmake in your build dir. Alternatively you can use the -DVARIABLE=value syntax in the previous step.
4. run 'make -j ' to build with N parallel jobs.
5. Now is a good time to get a coffee.

You are most likely to want to specify the GPU architecture of the machine you are building for. Either configure QUDA_GPU_ARCH in step 3 or specify e.g. -DQUDA_GPU_ARCH=sm_60 for a Pascal GPU in step 2.

Multi-GPU support

QUDA supports using multiple GPUs through MPI and QMP. To enable multi-GPU support either set QUDA_MPI or QUDA_QMP to ON when configuring QUDA through cmake.

Note that in any case cmake will automatically try to detect your MPI installation. If you need to specify a particular MPI please set MPI_C_COMPILER and MPI_CXX_COMPILER in cmake. See also https://cmake.org/cmake/help/v3.9/module/FindMPI.html for more help.

For QMP please set QUDA_QMP_HOME to the installation directory of QMP.

For more details see https://github.com/lattice/quda/wiki/Multi-GPU-Support

External dependencies

The eigen-vector solvers (eigCG and incremental eigCG) by default will use Eigen, however, QUDA can be configured to use MAGMA if available (see https://github.com/lattice/quda/wiki/Deflated-Solvers for more details). MAGMA is available from http://icl.cs.utk.edu/magma/index.html. MAGMA is enabled using the cmake option QUDA_MAGMA=ON.

Version 0.9.x of QUDA includes interface for the external (P)ARPACK library for eigenvector computing. (P)ARPACK is available, e.g., from https://github.com/opencollab/arpack-ng. (P)ARPACK is enabled using CMake option QUDA_ARPACK=ON. Note that with a multi-gpu option, the build system will automatically use PARPACK library.

Application Interfaces

By default only the QDP and MILC interfaces are enabled. For interfacing support with QDPJIT, BQCD or CPS; this should be enabled at by setting the corresponding QUDA_INTERFACE_<application> variable e.g., QUDA_INTERFACE_BQCD=ON. To keep compilation time to a minimum it is recommended to only enable those interfaces that are used by a given application.

Tuning

Throughout the library, auto-tuning is used to select optimal launch parameters for most performance-critical kernels. This tuning process takes some time and will generally slow things down the first time a given kernel is called during a run. To avoid this one-time overhead in subsequent runs (using the same action, solver, lattice volume, etc.), the optimal parameters are cached to disk. For this to work, the QUDA_RESOURCE_PATH environment variable must be set, pointing to a writable directory. Note that since the tuned parameters are hardware- specific, this "resource directory" should not be shared between jobs running on different systems (e.g., two clusters with different GPUs installed). Attempting to use parameters tuned for one card on a different card may lead to unexpected errors.

This autotuning information can also be used to build up a first-order kernel profile: since the autotuner measures how long a kernel takes to run, if we simply keep track of the number of kernel calls, from the product of these two quantities we have a time profile of a given job run. If QUDA_RESOURCE_PATH is set, then this profiling information is output to the file "profile.tsv" in this specified directory. Optionally, the output filename can be specified using the QUDA_PROFILE_OUTPUT environment variable, to avoid overwriting previously generated profile outputs. In addition to the kernel profile, a policy profile, e.g., collections of kernels and/or other algorithms that are auto-tuned, is also output to the file "profile_async_async.tsv". The policy profile for example includes the entire multi-GPU dslash, whose style and order of communication is autotuned. Hence while the dslash kernel entries appearing the kernel profile do include communucation time, the entries in the policy profile include all constituent parts (halo packing, interior update, communication and exterior update).

Using the Library:

Include the header file include/quda.h in your application, link against lib/libquda.a, and study tests/invert_test.cpp (for Wilson, clover, twisted-mass, or domain wall fermions) or tests/staggered_invert_test.cpp (for asqtad/HISQ fermions) for examples of the solver interface. The various solver options are enumerated in include/enum_quda.h.

Known Issues:

• For compatibility with CUDA, on 32-bit platforms the library is compiled with the GCC option -malign-double. This differs from the GCC default and may affect the alignment of various structures, notably those of type QudaGaugeParam and QudaInvertParam, defined in quda.h. Therefore, any code to be linked against QUDA should also be compiled with this option.

• When the auto-tuner is active in a multi-GPU run it may cause issues with binary reproducibility of this run if domain-decomposition preconditioning is used. This is caused by the possibility of different launch configurations being used on different GPUs in the tuning run simultaneously. If binary reproducibility is strictly required make sure that a run with active tuning has completed. This will ensure that the same launch configurations for a given kernel is used on all GPUs and binary reproducibility.

Getting Help:

Please visit http://lattice.github.com/quda for contact information. Bug reports are especially welcome.

Acknowledging QUDA:

If you find this software useful in your work, please cite:

M. A. Clark, R. Babich, K. Barros, R. Brower, and C. Rebbi, "Solving Lattice QCD systems of equations using mixed precision solvers on GPUs," Comput. Phys. Commun. 181, 1517 (2010) [arXiv:0911.3191 [hep-lat]].

When taking advantage of multi-GPU support, please also cite:

R. Babich, M. A. Clark, B. Joo, G. Shi, R. C. Brower, and S. Gottlieb, "Scaling lattice QCD beyond 100 GPUs," International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2011 [arXiv:1109.2935 [hep-lat]].

When taking advantage of adaptive multigrid, please also cite:

M. A. Clark, A. Strelchenko, M. Cheng, A. Gambhir, and R. Brower, "Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization," International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2016 [arXiv:1612.07873 [hep-lat]].

When taking advantage of block CG, please also cite:

M. A. Clark, A. Strelchenko, A. Vaquero, M. Wagner, and E. Weinberg, "Pushing Memory Bandwidth Limitations Through Efficient Implementations of Block-Krylov Space Solvers on GPUs," To be published in Comput. Phys. Commun. (2018) [arXiv:1710.09745 [hep-lat]].

Several other papers that might be of interest are listed at http://lattice.github.com/quda .

Authors:

• Ronald Babich (NVIDIA)
• Simone Bacchio (Cyprus)
• Michael Baldhauf (Regensburg)
• Kipton Barros (Los Alamos National Laboratory)
• Richard Brower (Boston University)
• Nuno Cardoso (NCSA)
• Kate Clark (NVIDIA)
• Michael Cheng (Boston University)
• Carleton DeTar (Utah University)
• Justin Foley (NIH)
• Joel Giedt (Rensselaer Polytechnic Institute)
• Arjun Gambhir (William and Mary)
• Steven Gottlieb (Indiana University)
• Kyriakos Hadjiyiannakou (Cyprus)
• Dean Howarth (Boston University)
• Balint Joo (Jefferson Laboratory)
• Hyung-Jin Kim (Samsung Advanced Institute of Technology)
• Bartek Kostrzewa (Bonn)
• Claudio Rebbi (Boston University)
• Guochun Shi (NCSA)
• Hauke Sandmeyer (Bielefeld)
• Mario Schröck (INFN)
• Alexei Strelchenko (Fermi National Accelerator Laboratory)
• Alejandro Vaquero (Utah University)
• Mathias Wagner (NVIDIA)
• Evan Weinberg (NVIDIA)
• Frank Winter (Jlab)

Portions of this software were developed at the Innovative Systems Lab, National Center for Supercomputing Applications http://www.ncsa.uiuc.edu/AboutUs/Directorates/ISL.html

Development was supported in part by the U.S. Department of Energy under grants DE-FC02-06ER41440, DE-FC02-06ER41449, and DE-AC05-06OR23177; the National Science Foundation under grants DGE-0221680, PHY-0427646, PHY-0835713, OCI-0946441, and OCI-1060067; as well as the PRACE project funded in part by the EUs 7th Framework Programme (FP7/2007-2013) under grants RI-211528 and FP7-261557. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Energy, the National Science Foundation, or the PRACE project.