--- title: "Mathematica on Easley" description: "Derive exact beam-deflection formulas with Mathematica, then run the examples across CPUs, nodes, and a GPU on Easley." type: Tutorial tags: - Mathematica - Wolfram Language - Slurm - Easley - GPU - Scientific computing generated: by: "process:codex" at: "2026-09-29T00:00:00Z" --- # Mathematica on Easley Mathematica combines symbolic mathematics with numerical computing. This tutorial distributes symbolic derivations across CPUs and nodes, then evaluates many load combinations on a GPU. The distinguishing feature is the workflow: solve differential equations with symbolic parameters, manipulate the resulting formulas, and then evaluate them numerically in the same language. MATLAB, Julia, and R also have symbolic tools; this example highlights Mathematica's built-in symbolic solver. ## Files and setup Download [the complete example archive (ZIP)](https://carc.unm.edu/docs/assets/files/mathematica/mathematica-examples.zip), extract it, and work in that directory on Easley. Keep it on a filesystem accessible from compute nodes. ```bash wget https://carc.unm.edu/docs/assets/files/mathematica/mathematica-examples.zip unzip mathematica-examples.zip cd mathematica-examples ``` The archive includes the three Slurm scripts, Wolfram Language inputs, and the shared launcher. Run the jobs below one at a time. The examples use `neutrino.phys.unm.edu`; please substitute the fully qualified hostname of your Mathematica license server. The shared `math-kernel.sh` launcher contains: ```bash #!/bin/bash # Use the same license server for the controller and every worker. # Replace neutrino.phys.unm.edu with your license server's FQDN. exec math -pwfile <(printf '!neutrino.phys.unm.edu\n') "$@" ``` The Bash inline file supplies the license information without creating a persistent license file. `"$@"` forwards the kernel's arguments. Both the controller and parallel workers use this launcher, so each gets the same server. See Wolfram's [kernel documentation](https://reference.wolfram.com/language/ref/program/WolframKernel.html){target=_blank} for `-pwfile` details. ## Example: Beam Deflection For a simply supported beam, the small-deflection model is: > *EI* d⁴*y*/d*x*⁴ = *q*₀ (*x*/L)ⁿ with simply supported boundary conditions: > *y*(0) = *y*(L) = 0; d²*y*/d*x*²(0) = d²*y*/d*x*²(L) = 0 Here, `y(x)` is downward deflection, `length` is the beam length, `load` is the load-intensity scale, and `rigidity` is the flexural rigidity (`E × I`). The ends have zero deflection and zero bending moment. Setting `n = 0` gives a uniform load; `n = 1` gives a linearly increasing load. The shared `beam-model.wl` input derives the formula without assigning numerical values to these parameters: ```wolfram (* Simply supported beam: load (x/length)^power, constant flexural rigidity. *) Clear[beamDeflection, x, length, load, rigidity]; beamDeflection[power_Integer] := Module[{y}, Factor[DSolveValue[{ rigidity y''''[x] == load (x/length)^power, y[0] == 0, y[length] == 0, y''[0] == 0, y''[length] == 0 }, y[x], x]] ]; ``` `DSolveValue` solves the differential equation and its boundary conditions. `Factor` simplifies the resulting polynomial. For a uniform load, the exact midpoint deflection is: > *y*(L/2) = 5*q*₀L⁴/(384 *EI*) This formula describes a family of beams and loads rather than one numerical case. ## Multiple CPUs on one node `parallel.wl` launches two local workers and gives them the definition from `beam-model.wl`. `ParallelTable` derives midpoint formulas for load powers 0, 1, 2, and 3. It also prints each worker's hostname and license server. ```wolfram title="parallel.wl" workers = ToExpression[Environment["SLURM_CPUS_PER_TASK"]] - 1; launcher = FileNameJoin[{Directory[], "math-kernel.sh"}]; LaunchKernels[KernelConfiguration["Local", "KernelCommand" -> ("\"" <> launcher <> "\""), "KernelCount" -> workers]]; If[Length[Kernels[]] != workers, Print["Worker launch failed"]; Exit[1]]; Print["Workers: ", ParallelEvaluate[{$KernelID, $MachineName}]]; Print["Worker license servers: ", ParallelEvaluate[$LicenseServer]]; Get["beam-model.wl"]; DistributeDefinitions[beamDeflection]; midpoints = ParallelTable[ Factor[beamDeflection[n] /. x -> length/2], {n, 0, 3}]; Print["Midpoint formulas (load powers 0 through 3): ", ToString[midpoints, InputForm]]; CloseKernels[]; Exit[]; ``` `mathematica_multicpu.sbatch` contains: ```bash #!/bin/bash -l #SBATCH --job-name=mathematica-multicpu #SBATCH --partition=debug #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=3 #SBATCH --mem=4G #SBATCH --time=00:05:00 #SBATCH --output=%x-%j.out module load mathematica/15.0.1 cd "$SLURM_SUBMIT_DIR" chmod +x math-kernel.sh export OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 srun ./math-kernel.sh -script parallel.wl ``` Submit it with: ```bash sbatch mathematica_multicpu.sbatch ``` Three CPUs provide one for the controller and two for workers. The thread settings limit each worker's math libraries to one thread. To request more workers, increase `--cpus-per-task`; the input reserves one CPU for the controller. Available licenses also limit the worker count. The output includes two workers on the same node and these four exact formulas: ```text {5 load length^4/(384 rigidity), 5 load length^4/(768 rigidity), 89 load length^4/(23040 rigidity), 13 load length^4/(5120 rigidity)} ``` ### Measured scaling CARC timed 128 independent symbolic beam calculations on Easley with Mathematica 15.0.1. Each result is the median of three runs after kernel creation and warm-up; every requested worker processed tasks and produced the same formulas. | Workers | Median calculation time | Speedup | | ---: | ---: | ---: | | 1 | 2.21 s | 1.00× | | 2 | 1.21 s | 1.83× | | 4 | 0.59 s | 3.77× | ## Multiple nodes `distributed.wl` performs the same four derivations with one worker on each of two nodes. It uses Slurm to start the workers and Mathematica's WSTP protocol to connect them to the controller. ```wolfram title="distributed.wl" nodes = StringSplit[RunProcess[ {"scontrol", "show", "hostnames", Environment["SLURM_JOB_NODELIST"]}, "StandardOutput"]]; launcher = FileNameJoin[{Directory[], "math-kernel.sh"}]; links = Table[LinkCreate[LinkProtocol -> "TCPIP"], {Length[nodes]}]; processes = Table[ StartProcess[{ "srun", "--exact", "--nodes=1", "--ntasks=1", "--cpus-per-task=1", "--nodelist=" <> nodes[[i]], "--output=worker-%j-%N.out", "--error=worker-%j-%N.err", launcher, "-subkernel", "-noinit", "-wstp", "-linkmode", "Connect", "-linkprotocol", "TCPIP", "-linkname", First[links[[i]]] }], {i, Length[nodes]} ]; TimeConstrained[LaunchKernels[links], 90, Exit[1]]; If[Length[Kernels[]] != Length[nodes], Exit[1]]; Print["Workers: ", ParallelEvaluate[{$KernelID, $MachineName}]]; Print["Worker license servers: ", ParallelEvaluate[$LicenseServer]]; Get["beam-model.wl"]; DistributeDefinitions[beamDeflection]; midpoints = ParallelTable[ Factor[beamDeflection[n] /. x -> length/2], {n, 0, 3}]; Print["Midpoint formulas (load powers 0 through 3): ", ToString[midpoints, InputForm]]; Scan[LinkWrite[#, Unevaluated[EvaluatePacket[Quit[0]]]] &, links]; TimeConstrained[ While[AnyTrue[processes, ProcessStatus[#] === "Running" &], Pause[0.1]], 20, Exit[1] ]; codes = ProcessInformation[#, "ExitCode"] & /@ processes; CloseKernels[]; If[codes =!= ConstantArray[0, Length[nodes]], Exit[1]]; Exit[]; ``` `mathematica_multinode.sbatch` contains: ```bash #!/bin/bash -l #SBATCH --job-name=mathematica-multinode #SBATCH --partition=debug #SBATCH --nodes=2 #SBATCH --ntasks-per-node=2 #SBATCH --cpus-per-task=1 #SBATCH --mem=4G #SBATCH --time=00:05:00 #SBATCH --output=%x-%j.out module load mathematica/15.0.1 cd "$SLURM_SUBMIT_DIR" chmod +x math-kernel.sh export OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 ./math-kernel.sh -script distributed.wl ``` Submit it with: ```bash sbatch mathematica_multinode.sbatch ``` The controller runs directly in the batch step. The input starts separate `srun` steps for its workers; adding `srun` in front of the controller would start duplicate controllers. Each worker uses the same shared license launcher. Expect the same formulas as above, but worker hostnames on **two different nodes**. Worker messages are saved in `worker-*.out` and `worker-*.err`. The input includes a connection timeout and closes workers before exiting. ## GPU evaluation The GPU example uses the symbolic formulas as input to a numerical calculation. It does not run the symbolic differential-equation solver on the GPU. `gpu.wl` derives four beam-response formulas on the CPU. It sets `length = load = rigidity = 1` to obtain normalized response shapes, samples them at 256 positions, and combines them using 1,024 sets of load coefficients: ```wolfram gpuResult = CUDADot[shapeMatrix, coefficients]; ``` This GPU matrix multiplication produces deflection at every position for every load combination. Superposition applies because the beam equation is linear. The example compares the GPU result with a CPU multiplication and prints the maximum difference. See Wolfram's [CUDADot documentation](https://reference.wolfram.com/language/CUDALink/ref/CUDADot.html){target=_blank}. ```wolfram title="gpu.wl" Get["beam-model.wl"]; Needs["CUDALink`"]; If[!TrueQ[CUDAQ[]], Print["CUDA unavailable"]; Exit[1]]; (* Derive exact responses for four polynomial loads, then normalize the units. *) shapes = Table[ beamDeflection[n] /. {length -> 1, load -> 1, rigidity -> 1}, {n, 0, 3} ]; Print["Uniform-load formula: ", ToString[shapes[[1]], InputForm]]; (* Evaluate at 256 positions for 1024 combinations of the four loads. *) positions = N[Subdivide[0, 1, 255]]; shapeMatrix = Table[shapes /. x -> position, {position, positions}]; coefficients = N[Table[Sin[i j/100], {i, 1, 4}, {j, 1, 1024}]]; gpuResult = CUDADot[shapeMatrix, coefficients]; difference = Max[Abs[Flatten[gpuResult - shapeMatrix.coefficients]]]; Print["License server: ", $LicenseServer]; Print["Result dimensions: ", Dimensions[gpuResult]]; Print["Maximum difference from CPU: ", difference]; If[difference > 10^-10, Exit[1]]; Exit[]; ``` `mathematica_gpu.sbatch` contains: ```bash #!/bin/bash -l #SBATCH --job-name=mathematica-gpu #SBATCH --partition=debug #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=1 #SBATCH --gpus-per-node=1 #SBATCH --mem=4G #SBATCH --time=00:05:00 #SBATCH --output=%x-%j.out module load mathematica/15.0.1 cd "$SLURM_SUBMIT_DIR" chmod +x math-kernel.sh srun ./math-kernel.sh -script gpu.wl ``` Submit it with: ```bash sbatch mathematica_gpu.sbatch ``` Expect result dimensions `{256, 1024}` and a maximum CPU/GPU difference close to zero. Small floating-point differences are normal. This example checks the CPU-to-GPU workflow; a small workload may be faster on the CPU. On first use, CUDALink may download supporting software into your Wolfram cache, which can add startup time. ## Read the results `sbatch` prints the job number. Check progress with: ```bash squeue --me ``` Each job writes `mathematica--.out` in the submission directory. For example: ```bash cat mathematica-multicpu-1235085.out ``` Use your own job number. If a job reports `No valid password found`, check the hostname in `math-kernel.sh` and license availability. If a two-node debug job is pending with `QOSMaxNodePerUserLimit`, let your other debug jobs finish. ## Further reading - [Wolfram Language](https://reference.wolfram.com/language/){target=_blank} - [Parallel kernel configuration](https://reference.wolfram.com/language/ref/KernelConfiguration.html){target=_blank} - [CUDALink](https://reference.wolfram.com/language/CUDALink/tutorial/Overview.html){target=_blank} - [Introduction to Slurm](https://carc.unm.edu/docs/running-jobs/slurm-intro/)