Getting good performance from mdrun

The GROMACS build system and the gmx mdrun tool has a lot of built-in and configurable intelligence to detect your hardware and make pretty effective use of that hardware. For a lot of casual and serious use of gmx mdrun, the automatic machinery works well enough. But to get the most from your hardware to maximize your scientific quality, read on!

Hardware background information

Modern computer hardware is complex and heterogeneous, so we need to discuss a little bit of background information and set up some definitions. Experienced HPC users can skip this section.

A hardware compute unit that actually executes instructions. There is normally more than one core in a processor, often many more.
A special kind of memory local to core(s) that is much faster to access than main memory, kind of like the top of a human’s desk, compared to their filing cabinet. There are often several layers of caches associated with a core.
A group of cores that share some kind of locality, such as a shared cache. This makes it more efficient to spread computational work over cores within a socket than over cores in different sockets. Modern processors often have more than one socket.
A group of sockets that share coarser-level locality, such as shared access to the same memory without requiring any network hardware. A normal laptop or desktop computer is a node. A node is often the smallest amount of a large compute cluster that a user can request to use.
A stream of instructions for a core to execute. There are many different programming abstractions that create and manage spreading computation over multiple threads, such as OpenMP, pthreads, winthreads, CUDA, OpenCL, and OpenACC. Some kinds of hardware can map more than one software thread to a core; on Intel x86 processors this is called “hyper-threading”, while the more general concept is often called SMT for “simultaneous multi-threading”. IBM Power8 can for instance use up to 8 hardware threads per core. This feature can usually be enabled or disabled either in the hardware bios or through a setting in the Linux operating system. GROMACS can typically make use of this, for a moderate free performance boost. In most cases it will be enabled by default e.g. on new x86 processors, but in some cases the system administrators might have disabled it. If that is the case, ask if they can re-enable it for you. If you are not sure if it is enabled, check the output of the CPU information in the log file and compare with CPU specifications you find online.
thread affinity (pinning)
By default, most operating systems allow software threads to migrate between cores (or hardware threads) to help automatically balance workload. However, the performance of gmx mdrun can deteriorate if this is permitted and will degrade dramatically especially when relying on multi-threading within a rank. To avoid this, gmx mdrun will by default set the affinity of its threads to individual cores/hardware threads, unless the user or software environment has already done so (or not the entire node is used for the run, i.e. there is potential for node sharing). Setting thread affinity is sometimes called thread “pinning”.
The dominant multi-node parallelization-scheme, which provides a standardized language in which programs can be written that work across more than one node.
In MPI, a rank is the smallest grouping of hardware used in the multi-node parallelization scheme. That grouping can be controlled by the user, and might correspond to a core, a socket, a node, or a group of nodes. The best choice varies with the hardware, software and compute task. Sometimes an MPI rank is called an MPI process.
A graphics processing unit, which is often faster and more efficient than conventional processors for particular kinds of compute workloads. A GPU is always associated with a particular node, and often a particular socket within that node.
A standardized technique supported by many compilers to share a compute workload over multiple cores. Often combined with MPI to achieve hybrid MPI/OpenMP parallelism.
A proprietary parallel computing framework and API developed by NVIDIA that allows targeting their accelerator hardware. GROMACS uses CUDA for GPU acceleration support with NVIDIA hardware.
An open standard-based parallel computing framework that consists of a C99-based compiler and a programming API for targeting heterogeneous and accelerator hardware. GROMACS uses OpenCL for GPU acceleration on AMD devices (both GPUs and APUs); NVIDIA hardware is also supported.
Modern CPU cores have instructions that can execute large numbers of floating-point instructions in a single cycle.

GROMACS background information

The algorithms in gmx mdrun and their implementations are most relevant when choosing how to make good use of the hardware. For details, see the Reference Manual. The most important of these are

Domain Decomposition
The domain decomposition (DD) algorithm decomposes the (short-ranged) component of the non-bonded interactions into domains that share spatial locality, which permits the use of efficient algorithms. Each domain handles all of the particle-particle (PP) interactions for its members, and is mapped to a single MPI rank. Within a PP rank, OpenMP threads can share the workload, and some work can be off-loaded to a GPU. The PP rank also handles any bonded interactions for the members of its domain. A GPU may perform work for more than one PP rank, but it is normally most efficient to use a single PP rank per GPU and for that rank to have thousands of particles. When the work of a PP rank is done on the CPU, mdrun will make extensive use of the SIMD capabilities of the core. There are various command-line options <controlling-the-domain-decomposition-algorithm to control the behaviour of the DD algorithm.
Particle-mesh Ewald
The particle-mesh Ewald (PME) algorithm treats the long-ranged components of the non-bonded interactions (Coulomb and/or Lennard-Jones). Either all, or just a subset of ranks may participate in the work for computing long-ranged component (often inaccurately called simple the “PME” component). Because the algorithm uses a 3D FFT that requires global communication, its performance gets worse as more ranks participate, which can mean it is fastest to use just a subset of ranks (e.g. one-quarter to one-half of the ranks). If there are separate PME ranks, then the remaining ranks handle the PP work. Otherwise, all ranks do both PP and PME work.

Running mdrun within a single node

gmx mdrun can be configured and compiled in several different ways that are efficient to use within a single node. The default configuration using a suitable compiler will deploy a multi-level hybrid parallelism that uses CUDA, OpenMP and the threading platform native to the hardware. For programming convenience, in GROMACS, those native threads are used to implement on a single node the same MPI scheme as would be used between nodes, but much more efficient; this is called thread-MPI. From a user’s perspective, real MPI and thread-MPI look almost the same, and GROMACS refers to MPI ranks to mean either kind, except where noted. A real external MPI can be used for gmx mdrun within a single node, but runs more slowly than the thread-MPI version.

By default, gmx mdrun will inspect the hardware available at run time and do its best to make fairly efficient use of the whole node. The log file, stdout and stderr are used to print diagnostics that inform the user about the choices made and possible consequences.

A number of command-line parameters are available to modify the default behavior.

The total number of threads to use. The default, 0, will start as many threads as available cores. Whether the threads are thread-MPI ranks, and/or OpenMP threads within such ranks depends on other settings.
The total number of thread-MPI ranks to use. The default, 0, will start one rank per GPU (if present), and otherwise one rank per core.
The total number of OpenMP threads per rank to start. The default, 0, will start one thread on each available core. Alternatively, mdrun will honor the appropriate system environment variable (e.g. OMP_NUM_THREADS) if set.
The total number of ranks to dedicate to the long-ranged component of PME, if used. The default, -1, will dedicate ranks only if the total number of threads is at least 12, and will use around a quarter of the ranks for the long-ranged component.
When using PME with separate PME ranks, the total number of OpenMP threads per separate PME ranks. The default, 0, copies the value from -ntomp.
A string that specifies the ID numbers of the GPUs to be used by corresponding PP ranks on this node. For example, “0011” specifies that the lowest two PP ranks use GPU 0, and the other two use GPU 1.
Can be set to “auto,” “on” or “off” to control whether mdrun will attempt to set the affinity of threads to cores. Defaults to “auto,” which means that if mdrun detects that all the cores on the node are being used for mdrun, then it should behave like “on,” and attempt to set the affinities (unless they are already set by something else).
If -pin on, specifies the logical core number to which mdrun should pin the first thread. When running more than one instance of mdrun on a node, use this option to to avoid pinning threads from different mdrun instances to the same core.
If -pin on, specifies the stride in logical core numbers for the cores to which mdrun should pin its threads. When running more than one instance of mdrun on a node, use this option to to avoid pinning threads from different mdrun instances to the same core. Use the default, 0, to minimize the number of threads per physical core - this lets mdrun manage the hardware-, OS- and configuration-specific details of how to map logical cores to physical cores.
Can be set to “interleave,” “pp_pme” or “cartesian.” Defaults to “interleave,” which means that any separate PME ranks will be mapped to MPI ranks in an order like PP, PP, PME, PP, PP, PME, … etc. This generally makes the best use of the available hardware. “pp_pme” maps all PP ranks first, then all PME ranks. “cartesian” is a special-purpose mapping generally useful only on special torus networks with accelerated global communication for Cartesian communicators. Has no effect if there are no separate PME ranks.
Used to set where to execute the non-bonded interactions. Can be set to “auto”, “cpu”, “gpu”, “gpu_cpu.” Defaults to “auto,” which uses a compatible GPU if available. Setting “cpu” requires that no GPU is used. Setting “gpu” requires that a compatible GPU be available and will be used. Setting “gpu_cpu” lets the GPU compute the local and the CPU the non-local non-bonded interactions. Is only faster under a narrow range of conditions.

Examples for mdrun on one node

gmx mdrun

Starts mdrun using all the available resources. mdrun will automatically choose a fairly efficient division into thread-MPI ranks, OpenMP threads and assign work to compatible GPUs. Details will vary with hardware and the kind of simulation being run.

gmx mdrun -nt 8

Starts mdrun using 8 threads, which might be thread-MPI or OpenMP threads depending on hardware and the kind of simulation being run.

gmx mdrun -ntmpi 2 -ntomp 4

Starts mdrun using eight total threads, with four thread-MPI ranks and two OpenMP threads per core. You should only use these options when seeking optimal performance, and must take care that the ranks you create can have all of their OpenMP threads run on the same socket. The number of ranks must be a multiple of the number of sockets, and the number of cores per node must be a multiple of the number of threads per rank.

gmx mdrun -gpu_id 12

Starts mdrun using GPUs with IDs 1 and 2 (e.g. because GPU 0 is dedicated to running a display). This requires two thread-MPI ranks, and will split the available CPU cores between them using OpenMP threads.

gmx mdrun -ntmpi 4 -gpu_id "1122"

Starts mdrun using four thread-MPI ranks, and maps them to GPUs with IDs 1 and 2. The CPU cores available will be split evenly between the ranks using OpenMP threads.

gmx mdrun -nt 6 -pin on -pinoffset 0
gmx mdrun -nt 6 -pin on -pinoffset 3

Starts two mdrun processes, each with six total threads. Threads will have their affinities set to particular logical cores, beginning from the logical core with rank 0 or 3, respectively. The above would work well on an Intel CPU with six physical cores and hyper-threading enabled. Use this kind of setup only if restricting mdrun to a subset of cores to share a node with other processes.

mpirun -np 2 gmx_mpi mdrun

When using an gmx mdrun compiled with external MPI, this will start two ranks and as many OpenMP threads as the hardware and MPI setup will permit. If the MPI setup is restricted to one node, then the resulting gmx mdrun will be local to that node.

Running mdrun on more than one node

This requires configuring GROMACS to build with an external MPI library. By default, this mdrun executable is run with gmx mdrun. All of the considerations for running single-node mdrun still apply, except that -ntmpi and -nt cause a fatal error, and instead the number of ranks is controlled by the MPI environment. Settings such as -npme are much more important when using multiple nodes. Configuring the MPI environment to produce one rank per core is generally good until one approaches the strong-scaling limit. At that point, using OpenMP to spread the work of an MPI rank over more than one core is needed to continue to improve absolute performance. The location of the scaling limit depends on the processor, presence of GPUs, network, and simulation algorithm, but it is worth measuring at around ~200 particles/core if you need maximum throughput.

There are further command-line parameters that are relevant in these cases.

Defaults to “on.” If “on,” a Verlet-scheme simulation will optimize various aspects of the PME and DD algorithms, shifting load between ranks and/or GPUs to maximize throughput. Some mdrun features are not compatible with this, and these ignore this option.
Can be set to “auto,” “no,” or “yes.” Defaults to “auto.” Doing Dynamic Load Balancing between MPI ranks is needed to maximize performance. This is particularly important for molecular systems with heterogeneous particle or interaction density. When a certain threshold for performance loss is exceeded, DLB activates and shifts particles between ranks to improve performance.
During the simulation gmx mdrun must communicate between all ranks to compute quantities such as kinetic energy. By default, this happens whenever plausible, and is influenced by a lot of [.mdp options](#mdp-options). The period between communication phases must be a multiple of nstlist, and defaults to the minimum of nstcalcenergy and nstlist. mdrun -gcom sets the number of steps that must elapse between such communication phases, which can improve performance when running on a lot of ranks. Note that this means that _e.g._ temperature coupling algorithms will effectively remain at constant energy until the next communication phase. gmx mdrun will always honor the setting of mdrun -gcom, by changing nstcalcenergy, nstenergy, nstlog, nsttcouple and/or nstpcouple if necessary.

Note that -tunepme has more effect when there is more than one node, because the cost of communication for the PP and PME ranks differs. It still shifts load between PP and PME ranks, but does not change the number of separate PME ranks in use.

Note also that -dlb and -tunepme can interfere with each other, so if you experience performance variation that could result from this, you may wish to tune PME separately, and run the result with mdrun -notunepme -dlb yes.

The gmx tune_pme utility is available to search a wider range of parameter space, including making safe modifications to the tpr file, and varying -npme. It is only aware of the number of ranks created by the MPI environment, and does not explicitly manage any aspect of OpenMP during the optimization.

Examples for mdrun on more than one node

The examples and explanations for for single-node mdrun are still relevant, but -nt is no longer the way to choose the number of MPI ranks.

mpirun -np 16 gmx_mpi mdrun

Starts gmx mdrun with 16 ranks, which are mapped to the hardware by the MPI library, e.g. as specified in an MPI hostfile. The available cores will be automatically split among ranks using OpenMP threads, depending on the hardware and any environment settings such as OMP_NUM_THREADS.

mpirun -np 16 gmx_mpi mdrun -npme 5

Starts gmx mdrun with 16 ranks, as above, and require that 5 of them are dedicated to the PME component.

mpirun -np 11 gmx_mpi mdrun -ntomp 2 -npme 6 -ntomp_pme 1

Starts gmx mdrun with 11 ranks, as above, and require that six of them are dedicated to the PME component with one OpenMP thread each. The remaining five do the PP component, with two OpenMP threads each.

mpirun -np 4 gmx mdrun -ntomp 6 -gpu_id 00

Starts gmx mdrun on a machine with two nodes, using four total ranks, each rank with six OpenMP threads, and both ranks on a node sharing GPU with ID 0.

mpirun -np 8 gmx mdrun -ntomp 3 -gpu_id 0000

Using a same/similar hardware as above, starts gmx mdrun on a machine with two nodes, using eight total ranks, each rank with three OpenMP threads, and all four ranks on a node sharing GPU with ID 0. This may or may not be faster than the previous setup on the same hardware.

mpirun -np 20 gmx_mpi mdrun -ntomp 4 -gpu_id 0

Starts gmx mdrun with 20 ranks, and assigns the CPU cores evenly across ranks each to one OpenMP thread. This setup is likely to be suitable when there are ten nodes, each with one GPU, and each node has two sockets.

mpirun -np 20 gmx_mpi mdrun -gpu_id 00

Starts gmx mdrun with 20 ranks, and assigns the CPU cores evenly across ranks each to one OpenMP thread. This setup is likely to be suitable when there are ten nodes, each with one GPU, and each node has two sockets.

mpirun -np 20 gmx_mpi mdrun -gpu_id 01

Starts gmx mdrun with 20 ranks. This setup is likely to be suitable when there are ten nodes, each with two GPUs.

mpirun -np 40 gmx_mpi mdrun -gpu_id 0011

Starts gmx mdrun with 40 ranks. This setup is likely to be suitable when there are ten nodes, each with two GPUs, and OpenMP performs poorly on the hardware.

Controlling the domain decomposition algorithm

This section lists all the options that affect how the domain decomposition algorithm decomposes the workload to the available parallel hardware.

Can be used to set the required maximum distance for inter charge-group bonded interactions. Communication for two-body bonded interactions below the non-bonded cut-off distance always comes for free with the non-bonded communication. Particles beyond the non-bonded cut-off are only communicated when they have missing bonded interactions; this means that the extra cost is minor and nearly independent of the value of -rdd. With dynamic load balancing, option -rdd also sets the lower limit for the domain decomposition cell sizes. By default -rdd is determined by gmx mdrun based on the initial coordinates. The chosen value will be a balance between interaction range and communication cost.
On by default. When inter charge-group bonded interactions are beyond the bonded cut-off distance, gmx mdrun terminates with an error message. For pair interactions and tabulated bonds that do not generate exclusions, this check can be turned off with the option -noddcheck.
When constraints are present, option -rcon influences the cell size limit as well. Particles connected by NC constraints, where NC is the LINCS order plus 1, should not be beyond the smallest cell size. A error message is generated when this happens, and the user should change the decomposition or decrease the LINCS order and increase the number of LINCS iterations. By default gmx mdrun estimates the minimum cell size required for P-LINCS in a conservative fashion. For high parallelization, it can be useful to set the distance required for P-LINCS with -rcon.
Sets the minimum allowed x, y and/or z scaling of the cells with dynamic load balancing. gmx mdrun will ensure that the cells can scale down by at least this factor. This option is used for the automated spatial decomposition (when not using -dd) as well as for determining the number of grid pulses, which in turn sets the minimum allowed cell size. Under certain circumstances the value of -dds might need to be adjusted to account for high or low spatial inhomogeneity of the system.

Finding out how to run mdrun better

The Wallcycle module is used for runtime performance measurement of gmx mdrun. At the end of the log file of each run, the “Real cycle and time accounting” section provides a table with runtime statistics for different parts of the gmx mdrun code in rows of the table. The table contains colums indicating the number of ranks and threads that executed the respective part of the run, wall-time and cycle count aggregates (across all threads and ranks) averaged over the entire run. The last column also shows what precentage of the total runtime each row represents. Note that the gmx mdrun timer resetting functionalities (-resethway and -resetstep) reset the performance counters and therefore are useful to avoid startup overhead and performance instability (e.g. due to load balancing) at the beginning of the run.

The performance counters are:

  • Particle-particle during Particle mesh Ewald
  • Domain decomposition
  • Domain decomposition communication load
  • Domain decomposition communication bounds
  • Virtual site constraints
  • Send X to Particle mesh Ewald
  • Neighbor search
  • Launch GPU operations
  • Communication of coordinates
  • Born radii
  • Force
  • Waiting + Communication of force
  • Particle mesh Ewald
  • PME redist. X/F
  • PME spread/gather
  • PME 3D-FFT
  • PME 3D-FFT Communication
  • PME solve Lennard-Jones
  • PME solve Elec
  • PME wait for particle-particle
  • Wait + Receive PME force
  • Wait GPU nonlocal
  • Wait GPU local
  • Non-bonded position/force buffer operations
  • Virtual site spread
  • COM pull force
  • Write trajectory
  • Update
  • Constraints
  • Communication of energies
  • Enforced rotation
  • Add rotational forces
  • Position swapping
  • Interactive MD

As performance data is collected for every run, they are essential to assessing and tuning the performance of gmx mdrun performance. Therefore, they benefit both code developers as well as users of the program. The counters are an average of the time/cycles different parts of the simulation take, hence can not directly reveal fluctuations during a single run (although comparisons across multiple runs are still very useful).

Counters will appear in MD log file only if the related parts of the code were executed during the gmx mdrun run. There is also a special counter called “Rest” which indicated for the amount of time not accounted for by any of the counters above. Theerfore, a significant amount “Rest” time (more than a few percent) will often be an indication of parallelization inefficiency (e.g. serial code) and it is recommended to be reported to the developers.

An additional set of subcounters can offer more fine-grained inspection of performance. They are:

  • Domain decomposition redistribution
  • DD neighbor search grid + sort
  • DD setup communication
  • DD make topology
  • DD make constraints
  • DD topology other
  • Neighbor search grid local
  • NS grid non-local
  • NS search local
  • NS search non-local
  • Bonded force
  • Bonded-FEP force
  • Restraints force
  • Listed buffer operations
  • Nonbonded force
  • Ewald force correction
  • Non-bonded position buffer operations
  • Non-bonded force buffer operations

Subcounters are geared toward developers and have to be enabled during compilation. See Build system overview for more information.

TODO In future patch: - red flags in log files, how to interpret wallcycle output - hints to devs how to extend wallcycles

TODO In future patch: import wiki page stuff on performance checklist; maybe here, maybe elsewhere

Running mdrun with GPUs

NVIDIA GPUs from the professional line (Tesla or Quadro) starting with the Kepler generation (compute capability 3.5 and later) support changing the processor and memory clock frequency with the help of the applications clocks feature. With many workloads, using higher clock rates than the default provides significant performance improvements. For more information see the NVIDIA blog article on this topic. For GROMACS the highest application clock rates are optimal on all hardware available to date (up to and including Maxwell, compute capability 5.2).

Application clocks can be set using the NVIDIA system managemet tool nvidia-smi. If the system permissions allow, gmx mdrun has built-in support to set application clocks if built with NVML support. # TODO add ref to relevant section Note that application clocks are a global setting, hence affect the performance of all applications that use the respective GPU(s). For this reason, gmx mdrun sets application clocks at initialization to the values optimal for GROMACS and it restores them before exiting to the values found at startup, unless it detects that they were altered during its runtime.

Reducing overheads in GPU accelerated runs

In order for CPU cores and GPU(s) to execute concurrently, tasks are launched and executed asynchronously on the GPU(s) while the CPU cores execute non-offloaded force computation (like long-range PME electrostatics). Asynchronous task launches are handled by GPU device driver and require CPU involvement. Therefore, the work of scheduling GPU tasks will incur an overhead that can in some cases significantly delay or interfere with the CPU execution.

Delays in CPU execution are caused by the latency of launching GPU tasks, an overhead that can become significant as simulation ns/day increases (i.e. with shorter wall-time per step). The overhead is measured by gmx mdrun and reported in the performance summary section of the log file (“Launch GPU ops” row). A few percent of runtime spent in this category is normal, but in fast-iterating and multi-GPU parallel runs 10% or larger overheads can be observed. In general, there a user can do little to avoid such overheads, but there are a few cases where tweaks can give performance benefits. In single-rank runs timing of GPU tasks is by default enabled and, while in most cases its impact is small, in fast runs performance can be affected. The performance impact will be most significant on NVIDIA GPUs with CUDA, less on AMD with OpenCL. In these cases, when more than a few percent of “Launch GPU ops” time is observed, it is recommended turning off timing by setting the GMX_DISABLE_GPU_TIMING environment variable. In parallel runs with with many ranks sharing a GPU launch overheads can also be reduced by staring fewer thread-MPI or MPI ranks per GPU; e.g. most often one rank per thread or core is not optimal.

The second type of overhead, interference of the GPU driver with CPU computation, is caused by the scheduling and coordination of GPU tasks. A separate GPU driver thread can require CPU resources which may clash with the concurrently running non-offloaded tasks, potentially degrading the performance of PME or bonded force computation. This effect is most pronounced when using AMD GPUs with OpenCL with all stable driver releases to date (up to and including fglrx 12.15). To minimize the overhead it is recommended to leave a CPU hardware thread unused when launching gmx mdrun, especially on CPUs with high core count and/or HyperThreading enabled. E.g. on a machine with a 4-core CPU and eight threads (via HyperThreading) and an AMD GPU, try gmx mdrun -ntomp 7 -pin on. This will leave free CPU resources for the GPU task scheduling reducing interference with CPU computation. Note that assigning fewer resources to gmx mdrun CPU computation involves a tradeoff which may outweigh the benefits of reduced GPU driver overhead, in particular without HyperThreading and with few CPU cores.

TODO In future patch: any tips not covered above

Running the OpenCL version of mdrun

The current version works with GCN-based AMD GPUs, and NVIDIA CUDA GPUs. Make sure that you have the latest drivers installed. For AMD GPUs, Mesa version 17.0 or newer with LLVM 4.0 or newer is supported in addition to the proprietary driver. For NVIDIA GPUs, using the proprietary driver is required as the open source nouveau driver (available in Mesa) does not provide the OpenCL support. The minimum OpenCL version required is 1.1. See also the known limitations.

Devices from the AMD GCN architectures (all series) and NVIDIA Fermi and later (compute capability 2.0) are known to work, but before doing production runs always make sure that the GROMACS tests pass successfully on the hardware.

The OpenCL GPU kernels are compiled at run time. Hence, building the OpenCL program can take a few seconds introducing a slight delay in the gmx mdrun startup. This is not normally a problem for long production MD, but you might prefer to do some kinds of work, e.g. that runs very few steps, on just the CPU (e.g. see -nb above).

The same -gpu_id option (or GMX_GPU_ID environment variable) used to select CUDA devices, or to define a mapping of GPUs to PP ranks, is used for OpenCL devices.

Some other OpenCL management environment variables may be of interest to developers.

Known limitations of the OpenCL support

Limitations in the current OpenCL support of interest to GROMACS users:

  • No Intel devices (CPUs, GPUs or Xeon Phi) are supported
  • Due to blocking behavior of some asynchronous task enqueuing functions in the NVIDIA OpenCL runtime, with the affected driver versions there is almost no performance gain when using NVIDIA GPUs. The issue affects NVIDIA driver versions up to 349 series, but it known to be fixed 352 and later driver releases.
  • On NVIDIA GPUs the OpenCL kernels achieve much lower performance than the equivalent CUDA kernels due to limitations of the NVIDIA OpenCL compiler.
  • The AMD APPSDK version 3.0 ships with OpenCL compiler/runtime components, and (only in earlier releases), that conflict with newer fglrx GPU drivers which provide the same libraries. This conflict manifests in kernel launch failures as, due to the library path setup, the OpenCL runtime loads the APPSDK version of the aforementioned libraries instead of the ones provided by the driver installer. The recommended workaround is to remove or rename the APPSDK versions of the offending libraries.

Limitations of interest to GROMACS developers:

  • The current implementation is not compatible with OpenCL devices that are not using warp/wavefronts or for which the warp/wavefront size is not a multiple of 32
  • Some Ewald tabulated kernels are known to produce incorrect results, so (correct) analytical kernels are used instead.

Performance checklist

There are many different aspects that affect the performance of simulations in GROMACS. Most simulations require a lot of computational resources, therefore it can be worthwhile to optimize the use of those resources. Several issues mentioned in the list below could lead to a performance difference of a factor of 2. So it can be useful go through the checklist.

GROMACS configuration

  • Don’t use double precision unless you’re absolute sure you need it.
  • Compile the FFTW library (yourself) with the correct flags on x86 (in most cases, the correct flags are automatically configured).
  • On x86, use gcc or icc as the compiler (not pgi or the Cray compiler).
  • On POWER, use gcc instead of IBM’s xlc.
  • Use a new compiler version, especially for gcc (e.g. from the version 5 to 6 the performance of the compiled code improved a lot).
  • MPI library: OpenMPI usually has good performance and causes little trouble.
  • Make sure your compiler supports OpenMP (some versions of Clang don’t).
  • If you have GPUs that support either CUDA or OpenCL, use them.
    • Configure with -DGMX_GPU=ON (add -DGMX_USE_OPENCL=ON for OpenCL).
    • For CUDA, use the newest CUDA availabe for your GPU to take advantage of the latest performance enhancements.
    • Use a recent GPU driver.
    • If compiling on a cluster head node, make sure that GMX_CPU_ACCELERATION is appropriate for the compute nodes.

Run setup

  • For an approximately spherical solute, use a rhombic dodecahedron unit cell.
  • When using a time-step of 2 fs, use cutoff-scheme = h-bonds (and not all-bonds), since this is faster, especially with GPUs, and most force fields have been parametrized with only bonds involving hydrogens constrained.
  • You can increase the time-step to 4 or 5 fs when using virtual interaction sites (gmx pdb2gmx -vsite h).
  • For massively parallel runs with PME, you might need to try different numbers of PME ranks (gmx mdrun -npme ???) to achieve best performance; gmx tune_pme can help automate this search.
  • For massively parallel runs (also gmx mdrun -multidir), or with a slow network, global communication can become a bottleneck and you can reduce it with gmx mdrun -gcom (note that this does affect the frequency of temperature and pressure coupling).

Checking and improving performance

  • Look at the end of the md.log file to see the performance and the cycle counters and wall-clock time for different parts of the MD calculation. The PP/PME load ratio is also printed, with a warning when a lot of performance is lost due to imbalance.
  • Adjust the number of PME ranks and/or the cut-off and PME grid-spacing when there is a large PP/PME imbalance. Note that even with a small reported imbalance, the automated PME-tuning might have reduced the initial imbalance. You could still gain performance by changing the mdp parameters or increasing the number of PME ranks.
  • If the neighbor searching takes a lot of time, increase nstlist (with the Verlet cut-off scheme, this automatically adjusts the size of the neighbour list to do more non-bonded computation to keep energy drift constant).
    • If Comm. energies takes a lot of time (a note will be printed in the log file), increase nstcalcenergy or use mdrun -gcom.
    • If all communication takes a lot of time, you might be running on too many cores, or you could try running combined MPI/OpenMP parallelization with 2 or 4 OpenMP threads per MPI process.