Running MPI-enabled 2D 5pt Jacobi (-nperdomain S:10 means 10 MPI processes per socket, see likwid-mpirun documentation) The reference bandwidth of the STREAM benchmark is still valid, you just need to scale it up to the number of compute nodes. If you want to measure whether your MPI application cannot run faster because the memory controller of each compute node is saturated, you can use LIKWID again. If it is comparable, your code saturates the bandwidth.Īt cluster level you need special tools for the specific network technology if you want to track saturation in your network. | Memory data volume STAT | 941.4886 | 0 | 472.8975 | 13.0762 |Ĭompare the reported bandwidth with the one from your benchmarking run. When running your code, track the memory bandwidth with a suitable tool ( likwid-perfctr with L2, 元, and MEM setting, PAPI with L1*, L2*, 元* metrics or perf with L1*, LLC* and uncore_imc* events).Īs example, we use a 2D 5pt Jacobi stencil with LIKWID MarkerAPI instrumentation: For memory bandwidth on node-level, a common benchmark is STREAM.įunction Best Rate MB/s Avg time Min time Max time In order to detect saturated bandwidth, you need some reference value. Saturating speedup across cores sharing a data path.Since the source is shared by multiple consumers (cores), the consumers compete for new data (wait longer until data is transferred) and therefore have to reduce the number of work they can process. At cluster level, the shared data path is the interconnect network shared by all compute nodes.īandwidth Saturation means that the data source cannot provide more data per time interval. Inside CPU packages and whole compute nodes, the main resource for saturation is the memory subsystem but also the last level cache is a candidate. It depends on the system level which data path can be saturated. The pattern "Bandwidth Saturation" describes the performance limitation caused by fully utilizing a shared data path. 5 Applicable applications or algorithms or kernels.
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