Here we give an overview on the parallelization and acceleration schemes employed by GROMACS starting from version 4. Up to GROMACS 4. The aim is on the one hand, to provide an understanding of the underlying mechanism that make GROMACS one of the fastest molecular dynamics packages. In Gromacs the assembly and Fortran non-bonded kernels have been removed. These have been replaced by three levels of non-bonded kernels: reference or generic kernels, optimized "plain-C" kernels and SIMD intrinsic accelerated kernels. Other compute intensive parts of the code, mainly PME, bonded force calculation, and neighbor searching also employ SIMD intrinsic acceleration. Unlike the old assembly kernels, the new SIMD intrinsic code is compiled by the compiler. Technically, it is possible to compile different binary of acceleration for one binary, but this is difficult to manage with acceleration in many parts of the code. Thus, you need to configure gpu compile GROMACS with a single target hardware acceleration which corresponds to a SIMD instruction set. By default, the build system will detect the highest supported acceleration of the host where the compilation is carried out. Currently the supported acceleration options are: none, SSE2, SSE4. On x86, the performance difference between SSE2 and SSE4. Another effect of switching to intrinsics is that the choice of compiler now affects the performance. On x86 we advice the GNU compilers gcc version or later or Intel Compilers version 12 or later. At the time of writing, in most of our benchmarks we observed gcc to generate faster code. GROMACS, being performance-oriented, has a strong focus on efficient parallelization. As of version 4. Parallelization based on MPI has been part of GROMACS from the early versions hence is compatible with the majority of MD algorithms. At the heart of the MPI parallelization is the neutral-territory domain decomposition which supports fully automatic dynamic load balancing. When the work load is not balanced, some resources will be idling. An extreme example is GPU-only code such as OpenMM, where the CPU, which is always present, idles all the time. But how good the balance is will depend on your hardware and simulation setup. There are two extreme cases of imbalance: Below are examples that aim to show how the different parallelization schemes can be used with generation and later GROMACS versions. We assume default mdrun options wherever the explicit values are not specified. Note that all features available with MPI are binary supported with thread-MPI so whenever "process" or "MPI process" is used, these are equivalent. The OpenMP mutithreading enables utilizing the benefits of multicore machines wihtout. This parallelization is effectively equivalent with particle-decomposition. In GROMACS compiled with thread-MPI, OpenMP-only parallelization is the default with Verlet scheme when using up to 8 cores on AMD platforms and up to 12 and 16 cores on Intel Nehalem and Sandy Bridge, respectively. Note that even running across two CPUs in different sockets on Intel platforms OpenMP mutithreading is, in the majority of the cases, significantly faster than MPI-based parallelization. When all cores are used, mdrun will pin the threads to specific cores also known as setting the thread affinities for the coresunless it detects this has already been done e. This stops the operating system kernel from moving GROMACS processes between cores, which it might otherwise have done in response to non-GROMACS processes being run on the machine. Being able to move a GROMACS process when all cores have GROMACS processes is generally more wasteful than waiting for the old core to become free. If you want optimal performance when not using all cores, you need to use mdrun -pin on. This is particularly true if your hardware is heterogenous and not evenly divisible e. If you want to run multiple jobs on the same compute node, you need to limit the number of cores used and if you want good performance, pin different jobs to different cores. The mdrun option -nt sets the total number of threads for an mdrun job. The -pinoffset option sets a pinning offset, counted in logical numbers of cores. The only restriction with GPU runs is that the current parallelization scheme uses domain-decomposition to utilize multiple GPUs by assigning the computation of non-bonded forces in a domain to a GPU on the same physical node. Therefore, the number of GPUs used determines the domain-decompostion required, e. Consequently, you need to make sure binary start a number of MPI ranks that is a multiple of the number of GPUs intended to be used. With thread-MPI the number of MPI threads is automatically set to the number of compatible GPUs note that this could include slow GPUs. For instance, with an 8-core machine with two GPUs the launch command with thread-MPI can be as simple as: mdrun as in this case we detect the two GPUs, start two MPI threads with one GPU assigned to each. Although this scheme works well in the majority of cases, it does not take into account locality on the PCI-E bus and the performance of each GPU, each GPU will be assumed to have the same performance. As explained earlier, when using GPU acceleration, the short-range non-bonded forces are calculated on for GPU while the CPU calculated bonded forces and Ewald long-range electrostatics with PME. CPU cores working in parallel with the GPU need to belong to the same "team" of OpenMP threads, hence to the same MPI rank. Therefore, the number of GPUs in gpu compute node will typically detemine the number of PP MPI ranks needed, hence the number of threads per rank. The potential slowdowns get more pronounced when running in parallel on multiple compute nodes. In these cases, to address the bottleneck caused by multi-threading inefficiencies, it can be advantageous to reduce the number of OpenMP threads per rank. However, to not leave cores empty, this requires using more MPI ranks, hence more PP ranks, and therefore ranks will have to share GPUs. Such a configuration that runs many OpenMP threads per MPI rank will often be hampered by inefficient multithreading, e. To address this, we can try to run multiple MPI ranks per GPU with fewer threads each, e. There still needs to be a mapping of PP MPI ranks to GPU ids, but those PP ranks do not all have to come from the same component simulation. The mapping of MPI ranks into component simulations is distinct from the mapping of PP MPI ranks to GPUs. Note that it is most often advantageous to run multiple independent simulations either part of a multi-sim or not on a single GPU. In the single simulation per GPU case, the GPU utliilzation is limited to the amount of possible overlap between CPU and GPU computation during a time-step. In contrast, for simulations do not need to synchronize every time-step cand can singnificantly increase the overall GPU utilization. One physical core can support multiple logical cores or hardware threads. Accelerator, GPU Graphics processing units GPUs are powerful compute-accelerators with strong floating point capabilities. GROMACS gpu use of GPUs with the native GPU acceleration support in v The OpenMM-based acceleration, introduced in version 4. Thread-MPI, OpenMP Used in parallelization within a nodemultithreading enables efficient use of multicore CPUs. Multithreading was first introduced in GROMACS based on thread-MPI library which provides a threading-based MPI implementation. OpenMP-based multithreading is supported with GROMACS and can be combined with thread- MPI parallelization. With the native GPU acceleration support, GROMACS introduces hybrid parallelization. At the time of writing, in most of our benchmarks we observed gcc to generate faster code GPU acceleration GROMACS introduced the first version of GPU acceleration based on the OpenMM library. While this approach avoids the CPU-GPU communication bottleneck, it only supports a small subset of all GROMACS features and delivers substantial speedup compared to CPU runs only in case of implicit solvent simulations. The most compute-intensive part of simulations, the non-bonded force calculation can be offloaded a GPU and carried out simultaneously with CPU calculations of bonded forces and PME eletrostatics. Support is not limited to high-end cards and professional cards like Tesla and Quadro, GeForce cards work equally well. Although low-end GPUs e. GeForce GTX will work, typically at least a mid-class consumer GPU is needed to achieve speedup compared to CPU-only runs on a recent processor. For optimal performance with multiple GPUs, especially in multi-node runs, it is best to use identical hardware as balancing the for between different GPU is not possible. From GROMACSnative GPU acceleration supports now both CUDA and OpenCL. With CUDA, it is also optimized on Maxwell architectures CUDA Compute Capability OpenCL currently works well only in MacOS X and AMD GPUs. The Verlet scheme still includes only analytical non-bonded Van der Waals interactions. Soon tabulated potentials for non-bonded generic, Coulomb and Van der Waals will be fully supported in CUDA from GROMACS version x. Particle decomposition is also supported with MPI. To parallelize simulations across multiple machines e. Acting as a drop-in replacement for MPI, thread-MPI enables compiling and running mdrun on a single machine i. Additionally, it not only provides a convenient way to use computers with multicore CPU sbut thread-MPI does in some cases make mdrun run slightly faster than with MPI. Thread-MPI is compatible with most mdrun features and parallelization schemes, including OpenMP, GPUs; it is not compatible with MPI and multi-simulation runs. In a purely MPI-parallel scheme, all MPI processes use the same network interface, and although MPI intra-node communication is generally efficient, communication between nodes can become a limiting factor to parallelization. This is especially pronounced in the case of highly parallel simulations with PME which is very communication intensive and with "fat" nodes for by a slow network. To efficiently use all compute resource available, CPU and GPU computation is done simultaneously. Overlapping with the OpenMP multithreaded bonded force and PME long-range electrostatic calculations on the CPU, non-bonded forces are calculated on the GPU. Multiple GPUs, both in a single node as well as across multiple nodes, are supported using domain-decomposition. That the available CPU cores are partitioned among the processes or thread-MPI threads and a set of cores with a GPU do the calculations on the respective domain. With PME electrostatics, mdrun supports automated CPU-GPU load-balancing by shifting workload from the PME mesh calculations, done on the CPU, to the particle-particle non-bonded calculations, done on the GPU. At startup a few iterations of tuning are executed during the first to MD steps. These iterations involve scaling the electrostatics cut-off and PME grid spacing to determine the value that gives optimal CPU-GPU load balance. The Lennard-Jones cut-off rvdw is kept fixed. While the automated CPU-GPU load balancing always attempts to find the optimal cut-off setting, it might not always be possible to balance CPU and GPU workload. Binary are two extreme cases of imbalance Reaction-field simulations, especially with little bonded interaction, e. Here the CPU has almost nothing to do while the GPU calculates the non-bonded forces. In gpu future we plan to balance the non-bonded work load between GPU and CPU. Parallel simulations of a solvated macro-molecule with PME. When running on many GPUs, the domains corresponding to the protein will have a much higher work load, as with GPU acceleration the bonded forces start taking a significant amount of time. This leads to load imbalance and performance loss. Currently there is not much to do about this, except for placing your molecule and choosing the domain decomposition such that the molecule gets divided over multiple domains. We are working on a better solution for this issue Separate PME nodes By default, particle-particle PP and PME calculations are done in the same process one after another. As PME requires heavy global communication, this is most of the time the limiting factor to scaling on a large number of cores. By designating a subset of nodes for PME calcuations only, performance of parallel runs can be greatly improved. Using separate PME nodes has been possible since GROMACS With version OpenMP mutithreading in PME nodes is also possible and is suported with both group and verlet cut-off schemes. But node that modern communication networks can process several messages simultaneosly, such that it could be advantages to have more processes communicating. The number of PME binary is estimated by mdrun. If the PME load is higher than the PP load, mdrun will automatically balance the load, but this leads to additional non-bonded calculations. This avoids the idling of a large fraction of the nodes; usally of the nodes are PP nodes. The latter is especially useful when running on compute nodes with different number of cores as it enables setting different number of PME threads on different nodes Running simulations Below are examples that aim to show how the different parallelization schemes can be used with generation and later GROMACS versions. To set the number of threads for PME only independently from the number gpu threads in the rest of the code, there is the -ntomp option. With thread-MPI the number of MPI threads is automatically set to the number of compatible GPUs note that this could include slow GPUs For instance, with an 8-core machine with two GPUs the launch command with thread-MPI can be as simple as: mdrun as in this case we detect the two GPUs, start two MPI threads with one GPU assigned to each. However, on newer clusters with Sandy Bridge or Ivy Bridge processors with cores it is most of the time more advantageous to also share a GPU among multiple PP ranks In versionsthe measured and reported domain-decomposition load imbalance was usually incorrect when sharing GPUs, and tuning off load balancing -dlb no could actually improve performance in some cases. Fixed in Using multi-simulations and GPUs Using mdrun -multi to run multiple simulations in one call of mpirun e. While this approach avoids the CPU-GPU communication bottleneck, it only supports a small subset of all GROMACS features and delivers substantial speedup compared to CPU runs only in case of implicit solvent simulations With GROMACS 4. That the available CPU cores are partitioned among the processes or thread-MPI threads and a set of cores with a GPU do the calculations on the respective domain With PME electrostatics, mdrun supports automated CPU-GPU load-balancing by shifting workload from the PME mesh calculations, done on the CPU, to the particle-particle non-bonded calculations, done on the GPU. By designating a subset of nodes for PME calcuations only, performance of parallel runs can be greatly improved Using separate PME nodes has been possible since GROMACS With version OpenMP mutithreading in PME nodes is also possible and is suported with both group and verlet cut-off schemes.
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