By Alexander Supalov, Andrey Semin, Christopher Dahnken, Michael Klemm
Optimizing HPC purposes with Intel® Cluster instruments takes the reader on a journey of the fast-growing quarter of excessive functionality computing and the optimization of hybrid courses. those courses in most cases mix allotted reminiscence and shared reminiscence programming versions and use the Message Passing Interface (MPI) and OpenMP for multi-threading to accomplish the last word objective of excessive functionality at low energy intake on enterprise-class workstations and compute clusters.
The booklet makes a speciality of optimization for clusters which includes the Intel® Xeon processor, however the optimization methodologies additionally follow to the Intel® Xeon Phi™ coprocessor and heterogeneous clusters blending either architectures. in addition to the academic and reference content material, the authors deal with and refute many myths and misconceptions surrounding the subject. The textual content is augmented and enriched through descriptions of real-life situations.
What you’ll learn
- Practical, hands-on examples convey the way to make clusters and workstations in response to Intel® Xeon processors and Intel® Xeon Phi™ coprocessors "sing" in Linux environments
- How to grasp the synergy of Intel® Parallel Studio XE 2015 Cluster variation, such as Intel® Composer XE, Intel® MPI Library, Intel® hint Analyzer and Collector, Intel® VTune™ Amplifier XE, and plenty of different priceless tools
- How to accomplish speedy and tangible optimization effects whereas refining your figuring out of software program layout principles
Who this e-book is for
software program pros will use this ebook to layout, boost, and optimize their parallel courses on Intel systems. scholars of desktop technological know-how and engineering will worth the ebook as a entire reader, compatible to many optimization classes provided around the globe. The amateur reader will get pleasure from a radical grounding within the intriguing international of parallel computing.
Table of Contents
Foreword via Bronis de Supinski, CTO, Livermore Computing, LLNL
Chapter 1: No Time to learn this Book?
Chapter 2: evaluation of Platform Architectures
Chapter three: Top-Down software program Optimization
Chapter four: Addressing procedure Bottlenecks
Chapter five: Addressing program Bottlenecks: allotted Memory
Chapter 6: Addressing program Bottlenecks: Shared Memory
Chapter 7: Addressing software Bottlenecks: Microarchitecture
Chapter eight: software layout Considerations
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Additional info for Optimizing HPC Applications with Intel Cluster Tools: Hunting Petaflops
With this information to hand, you can find that there are power load imbalances among the task methods, and for you to specialise in making the MPI_Send operation as quick because it can visit in attaining a visible functionality hike. be aware that in the event you use the entire IPM package deal rather than the integrated records, additionally, you will get information at the overall verbal exchange quantity and floating element functionality that aren't measured by way of the Intel MPI Library. Optimize method Placement The Intel MPI Library places adjoining MPI ranks on one cluster node so long as there are cores to occupy. Use the Intel MPI command line argument -ppn to manage the method placement around the cluster nodes. for instance, this command will commence tactics according to node: $ mpirun -np sixteen -ppn 2 xhpl Intel MPI helps approach pinning to limit the MPI ranks to elements of the method with a purpose to optimize strategy format (for instance, to prevent NUMA results or to lessen latency to the InfiniBand adapter). Many suitable settings are defined within the Intel MPI Library Reference guide. nine in short, that allows you to run a natural MPI application in simple terms at the actual processor cores, input the next instructions: $ export I_MPI_PIN_PROCESSOR_LIST=allcores $ mpirun -np 2 your_MPI_app so one can run a hybrid MPI/OpenMP application, don’t swap the default Intel MPI settings, and spot the following part for the OpenMP ones. with a view to research Intel MPI procedure structure and pinning, set the subsequent surroundings variable: $ export I_MPI_DEBUG=4 Optimize Thread Placement If the appliance makes use of OpenMP for multithreading, you might have considered trying to manage thread placement as well as the method placement. attainable concepts are: $ export KMP_AFFINITY=granularity=thread,compact $ export KMP_AFFINITY=granularity=thread,scatter the 1st surroundings retains threads shut jointly to enhance inter-thread conversation, whereas the second one environment distributes the threads around the process to maximise reminiscence bandwidth. courses that use the OpenMP API model four. zero can use the an identical OpenMP affinity settings rather than the KMP_AFFINITY setting variable: $ export OMP_PROC_BIND=close $ export OMP_PROC_BIND=spread in case you use I_MPI_PIN_DOMAIN, MPI will confine the OpenMP threads of an MPI approach on a unmarried socket. you then can use the next environment to prevent thread circulation among the logical cores of the socket: $ export KMP_AFFINITY=granularity=thread Tuning Intel Composer XE when you've got entry to the resource code of the appliance, you could practice optimizations via deciding upon applicable compiler switches and recompiling the resource code. study Optimization and Vectorization experiences upload compiler flags -qopt-report and/or -vec-report to determine what the compiler did for your resource code. this can record all of the modifications utilized in your code. it is going to additionally spotlight these code styles that avoided winning optimization. handle them when you have time left. here's a small instance. as the optimization document can be quite lengthy, directory 1-2 in simple terms exhibits an excerpt from it.