The IBM POWER8i processor was designed for high performance on traditional server workloads as well as big data, analytics, and cloud workloads. In this paper, we describe key performance features of the IBM POWER8 processor. These include hardware assists that allow the POWER8 processor to automatically adapt to changing workloads by dynamically monitoring and tuning itself, enhancements to hardware instrumentation for performance monitoring, and performance improvements for encryption, virtualization, and I/O. We also describe the performance characteristics of a wide variety of applications, and we present the results of these applications running on POWER8 processor-based systems compared with previous generations of IBM Power Systemsi.Dynamic binary code optimization Feedback information has proven useful in guiding performance optimizations in compilers and post-link code optimizers. However, most statically compiled applications are not optimized with feedback-directed optimization (FDO [4]) for several reasons. For example, producing a
Coarse-grained multithreading, the switching of threads to avoid idle processor time during long-latency events, has been available on IBM systems since 1998. Simultaneous multithreading (SMT), first available on the POWER5e processor, moves beyond simple thread switching to the maintenance of two thread streams that are issued as continuously as possible to ensure the maximum use of processor resources. Because SMT has the potential of increasing processor efficiency and correspondingly increasing the amount of work done for a given time span, the reader might suppose that SMT would exhibit a performance gain for all workloads. This is true for most workloads, but is not true in some exceptional cases. In SMT mode, the processor resources-register sets, caches, queues, translation buffers, and the system memory nest-must be shared by both threads, and conditions can occur that degrade or even obviate SMT performance improvement. The POWER4e and POWER5 processors have very powerful performance monitor (PM) toolsets that can help the user to determine what is occurring in workloads that may not be providing expected SMT gains. In this paper, the results of measured differences among workloads having large, medium, small, and even negative SMT performance gains are presented along with an approach to investigating workloads to determine the source of SMT performance gain limits.
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