Behind the scenes, mainframes (IBM Z) and IBM Power Systems fuel banking, manufacturing, healthcare, insurance, transportation, and many other critical industries. While we each touch multiple silicon-based systems daily, enterprise systems are the ones you never see, but always rely on. In this talk, we'll look inside these systems, discuss the silicon technology, microarchitecture, and system elements which come together to deliver eight nines of system-level resiliency (99.999999% uptime). We'll explore the changes in workloads on these systems, and discuss how new drivers like AI for fraud detection, AI for insurance claims processing, and AI-driven system operations are changing enterprise requirements.

Spin-transfer torque magneto-resistive random-access memory (STT-MRAM) is an exciting new emerging technology, being considered as a strong candidate to fill the gaps in the existing memory hierarchy between DRAM and the secondary memory. STT-MRAM has adequate endurance. However, unresolved write switching and read reliability issues still exist at the functional operating temperature corners. One biggest challenge is that the read bit error rate (RBER) is not at an acceptable level for system reliability across the wide operating temperature range. We present an STT-MRAM memory subsystem that is fully compatible with existing DDR-based DIMM designs and evaluate read disturb and read sense bit-error rate (BER) under various operating temperature conditions. We propose temperature aware adaptive techniques for reliable reads at the rank level. The proposed temperature adaptation technique improves overall reliability of the DDR4 STT-MRAM-based memory subsystem with an optimal read current considering an acceptable 64-byte cacheline BER. Our full system simulations show 1000× order of improvements toward a cell raw read disturb BER along with 5% reduction in memory power and less than 1% impact on overall system performance.

Recent DRAM specifications exhibit increasing refresh latencies. A refresh command blocks a full rank, decreasing available parallelism in the memory subsystem significantly, thus decreasing performance. Fine Granularity Refresh (FGR) is a feature recently announced as part of JEDEC's DDR4 DRAM specification that attempts to tackle this problem by creating a range of refresh options that provide a trade-off between refresh latency and frequency.

In computer architecture, caches have primarily been viewed as a means to hide memory latency from the CPU. Cache policies have focused on anticipating the CPU's data needs, and are mostly oblivious to the main memory. In this paper, we demonstrate that the era of many-core architectures has created new main memory bottlenecks, and mandates a new approach: coordination of cache policy with main memory characteristics. Using the cache for memory optimization purposes, we propose a Virtual Write Queue which dramatically expands the memory controller's visibility of processor behavior, at low implementation overhead. Through memory-centric modification of existing policies, such as scheduled writebacks, this paper demonstrates that performance limiting effects of highly-threaded architectures can be overcome. We show that through awareness of the physical main memory layout and by focusing on writes, both read and write average latency can be shortened, memory power reduced, and overall system performance improved. Through full-system cycle-accurate simulations of SPEC cpu2006, we demonstrate that the proposed Virtual Write Queue achieves an average 10.9% system-level throughput improvement on memory-intensive workloads, along with an overall reduction of 8.7% in memory power across the whole suite.

High density memory is becoming more important as many execution streams are consolidated onto single chip many-core processors. DRAM is ubiquitous as a main memory technology, but while DRAM’s per-chip density and frequency continue to scale, the time required to refresh its dynamic cells has grown at an alarming rate. This paper shows how currently-employed methods to schedule refresh operations are ineffective in mitigating the significant performance degradation caused by longer refresh times. Current approaches are deficient– they do not effectively exploit the flexibility of DRAMs to postpone refresh operations. This work proposes dynamically reconfigurable predictive mechanisms that exploit the full dynamic range allowed in the JEDEC DDRx SDRAM specifications. The proposed mechanisms are shown to mitigate much of the penalties seen with dense DRAM devices. We refer to the overall scheme as Elastic Refresh, in that the refresh policy is stretched to fit the currently executing workload, such that the maximum benefit of the DRAM flexibility is realized. We extend the GEMS on SIMICS tool-set to include Elastic Refresh. Simulations show the proposed solution provides a 10% average performance improvement over existing techniques across the entire SPEC CPU suite, and up to a 41%improvement for certain workloads.

This paper describes and evaluates three architectural methods for accomplishing data parallel computation in a programmable embedded system. Comparisons are made between the well-studied Very Long Instruction Word (VLIW) and Single Instruction Multiple Packed Data (SIMpD) paradigms; the less-common Single Instruction Multiple Disjoint Data (SIMdD) architecture is described and evaluated. A taxonomy is defined for data-level parallel architectures, and patterns of data access for parallel computation are studied, with measurements presented for over 40 essential telecommunication and media kernels. While some algorithms exhibit data-level parallelism suited to packed vector computation, it is shown that other kernels are most efficiently scheduled with more flexible vector models. This motivates exploration of non-traditional processor architectures for the embedded domain.

Meaningful application benchmarking is crucial to processor design space exploration and compiler development. Many recent studies in the embedded processor domain have used reference telecommunications C programs which were originally intended to verify product compliance with a standard. In this paper, we demonstrate that telecommunications reference applications include a significant amount of superfluous code that skews a wide variety of experiments related to code size. A categorization of extra code is presented, and it is also demonstrated that benchmark inputs do not test broad usage patterns that might exist in a real system. It is shown that care must be taken in future studies to properly construct benchmarks which match the intended purpose of the target system and to provide inputs that more thoroughly exercise paths through telecommunications applications.

Media- and telecommunications-focused processors, increasingly designed as deeply pipelined, statically-scheduled VLIWs, rely on loop buffers for low-overhead execution of simple loops. Key loops containing control flow pose a substantial problem-full predication has a high encoding overhead, and partial predication techniques do not support if-conversion, the transformation of general acyclic control flow into predicated blocks. Using a set of significant media processing benchmarks, drawn from MediaBench and contemporary telecommunications standards, we explore a compromise approach. We demonstrate a compiler using if-conversion and specialized loop transformations to arrange for 70-99% of fetched operations to come from a simple, statically managed 256-instruction loop buffer, saving instruction fetch power and eliminating branch penalties. To complement this we introduce a "niche" form of predication specialized to permit general if-conversion with only a single bit in the encoding of each operation and to eliminate much of the hardware overhead of a predicate register-based approach.

Compiler-directed Computation Reuse (CCR) enhances program execution speed and efficiency by eliminating dynamic computation redundancy. In this approach, the compiler designates large program regions for potential reuse. During run time, the execution results of these reusable regions are recorded into hardware buffers for future reuse. Previous work shows that CCR can result in significant performance enhancements in general applications. A major limitation of the work is that the compiler relies on value profiling to identify reusable regions, making it difficult to deploy the scheme in many software production environments. This paper presents a new hardware model that alleviates the need for value profiling at compile time. The compiler is allowed to designate reusable regions that may prove to be inappropriate. The hardware mechanism monitors the dynamic behavior of compiler-designated regions and selectively activates the profitable ones at run time. Experimental results show that the proposed design makes more effective utilization of hardware buffer resources, achieves rapid employment of computation regions, and improves reuse accuracy, all of which promote more flexible compiler methods of identifying reusable computation regions.