11/5/2022 0 Comments Linpack benchmark failureThey are widely used by organizations to deploy their increasingly diverse workloads derived from modern applications such as big data, IoT, and edge/fog computing in either proprietary clusters or private, public cloud data centers. You should get improved FLOP performance compared to running HPL on a single node.Containers emerged as a lightweight alternative to virtual machines that offer better microservice architecture support. To run HPL with mpirun: mpirun -n 20 -f /nfs/hosts2. Our /nfs/hosts2 file contains 5 IP addresses. We chose 5 because our 6th node didn’t have Intel libraries at the time.Īfter editing the HPL.dat and saving the file, you can test using MPI with HPL. Q = 5, which is the number of nodes we use. P = 4, which is the max number of cores we have on each node. We used an N that was a multiple of 256 because we noticed huge performance drop when NBs < 256. When block sizes are too big, we can waste space and extra computation for the larger sizes. Small block sizes will limit the performance because there is less data reuse in the highest level of memory and more messaging. NB is the block size, which is used for data distribution and data reuse. NBs – subset of N to distribute across nodes. (P * Q is the total number of processes you can run on your cluster). On our cluster, our peak performance for N is at 64000. For instance, let’s say that I had 4 nodes with 256 MB of RAM each. If the problem size is too large, the performance will drop. You should fill up around 80% of total RAM as recommended by the HPL docs. The problem size is the largest problem size fitting in memory. The file contains information on the problem size, machine configuration, and algorithm. The HPL.dat file resides inside hpl/bin/xhpl. Very helpful notes on tuning HPL are available here. At least 50% of your cluster’s theoretical peak performance with HPL would be an excellent goal. Basically, things aren’t perfect when running HPL, so you won’t hit the theoretical peak, but the theoretical peak is a good number to base your results on. The performance results depend on the algorithm, size of the problem, implementation, human optimizations to the program, compiler’s optimizations, age of the compiler, the OS, interconnect, memory, architecture, and the hardware. Why are your performance results below the theoretical peak? You will have to do the same calculations for your GPU if you plan on running HPL with your GPU. Considering the CPU running of HPL, we would have a theoretical peak performance: 8 cores * 3.50 GHz * 16 FLOPs/cycle = 448 GFLOPS 32 SP FLOPs/cycle: two 8-wide FMA (fused multiply-add) instructionsĭP stands for double-precision, and SP stands for single-precision.16 DP FLOPs/cycle: two 4-wide FMA (fused multiply-add) instructions.There’s a Stackoverflow question listing the operations per cycle for a number of recent processor microarchitectures. Skylake is a name of a microarchitecture. After a little snooping on the page, I noticed a link stating Products formerly Skylake. Doing a Google search on Intel(R) Core(TM) i7-6700HQ CPU 2.60GHz, we find that the max frequency considering turbo is 3.5 GHz. For the operations per cycle, we need to dig deeper and search additional information about the architecture. From the model name, I see that I have a Intel(R) Core(TM) i7-6700HQ CPU 2.60GHz, which means the average frequency is 2.60GHz. cat /proc/cpuinfoĪt the bottom of the cpuinfo of my laptop, I see processor: 7, which means that we have 8 cores. First, we’ll look at the number of cores we have. You will come below the theoretical peak FLOPs/second, but the theoretical peak is a good number to compare your HPL results. With benchmarks like HPL, there is something called the theoretical peak FLOPs/s, which is denoted by: Number of cores * Average frequency * Operations per cycle When you run HPL, you will get a result with the number of FLOPs HPL took to complete. HPL is measured in FLOPs, which are floating point operations per second. HPL measures the floating point execution rate for solving a system of linear equations. For more information, visit the HPL FAQs. Here’s what we did to improve performance across nodes, but before we get into performance, let’s answer the big questions about HPL. For some reason, we would get the same performance with 1 node compared to 6 nodes. At first, we had difficulty improving HPL performance across nodes. The specs are: 6 x86 nodes each with an Intel(R) Xeon (R) CPU 5140 2.33 GHz, 4 cores, and no accelerators. We’ve been working on a benchmark called HPL also known as High Performance LINPACK on our cluster.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |