hpc

I have a piece of Python code where I use joblib and multiprocessing to make parts of the code run in parallel. I have no trouble running this on my desktop where I can use Task Manager to see that it uses all four cores and runs the code in parallel. I recently learnt that I have access to a HPC cluster with 100+ 20 core nodes. The cluster uses SLURM as the workload manager. The first question is: Is it possible to run parallelized Python code on a cluster? If it is possible, Does the Python code I have need to be changed at all to run on the cluster, and What #SBATCH instructions need to be

I'm testing a simple MPI program on my desktop (Ubuntu LTS 16.04/ Intel® Core™ i3-6100U CPU @ 2.30GHz × 4/ gcc 4.8.5 /OpenMPI 3.0.0) and mpirun won't let me use all of the cores on my machine (4). When I run: $ mpirun -n 4 ./test2 I get the following error: -------------------------------------------------------------------------- There are not enough slots available in the system to satisfy the 4 slots that were requested by the application: ./test2 Either request fewer slots for your application, or make more slots available for use. ----------------------------------------------------------

The title might be more specific than my actual problem is, although I believe answering this question would solve a more general problem, which is: how to decrease the effect of high latency (~700 cycle) that comes from random (but coalesced) global memory access in GPUs. In general if one accesses the global memory with coalesced load (eg. I read 128 consecutive bytes), but with very large distance (256KB-64MB) between coalesced accesses, one gets a high TLB (Translation Lookaside Buffer) miss rate. This high TLB miss rate is due to the limited number (~512) and size (~4KB) of the memory

I've instantiated a Hadoop 2.4.1 cluster and I've found that running MapReduce applications will parallelize differently depending on what kind of filesystem the input data is on. Using HDFS, a MapReduce job will spawn enough containers to maximize use of all available memory. For example, a 3-node cluster with 172GB of memory with each map task allocating 2GB, about 86 application containers will be created. On a filesystem that isn't HDFS (like NFS or in my use case, a parallel filesystem), a MapReduce job will only allocate a subset of available tasks (e.g., with the same 3-node cluster,

As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. As part of my Ph.D. research, I am working on development of numerical models of atmosphere and ocean circulation. These involve numerically solving systems of PDE's on the order of ~10^6 grid points, over ~10^4 time steps. Thus, a typical model simulation