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Internet web browsing has reached a critical tipping point. Increasingly, users rely more on mobile web browsers to access the Internet than desktop browsers. Meanwhile, webpages over the past decade have grown in complexity by more than tenfold. The fast penetration of mobile browsing and everricher webpages implies a growing need for high-performance mobile devices in the future to ensure continued end-user browsing experience. Failing to deliver webpages meeting hard cut-off constraints could directly translate to webpage abandonment or, for e-commerce websites, great revenue loss. However, mobile devices’ limited battery capacity limits the degree of performance that mobile web browsing can achieve. In this paper, we demonstrate the benefits of heterogeneous systems with big/little cores each with different frequencies to achieve the ideal trade-off between high performance and energy efficiency. Through detailed characterizations of different webpage primitives based on the hottest 5,000 webpages, we build statistical inference models that estimate webpage load time and energy consumption. We show that leveraging such predictive models lets us identify and schedule webpages using the ideal core and frequency configuration that minimizes energy consumption while still meeting stringent cut-off constraints. Real hardware and software evaluations show that our scheduling scheme achieves 83.0% energy savings, while only violating the cut-off latency for 4.1% more webpages as compared with a performance-oriented hardware strategy. Against a more intelligent, OS-driven, dynamic voltage and frequency scaling scheme, it achieves 8.6% energy savings and 4.0% performance improvement simultaneously.

Shrinking feature size and diminishing supply voltage are making circuits sensitive to supply voltage fluctuations within the microprocessor, caused by normal workload activity changes. If left unattended,voltage fluctuations can lead to timing violations or even transistor lifetime issues that degrade processor robustness. Mechanisms that learn to tolerate, avoid, and eliminate voltage fluctuations based on program and microarchitectural events can help steer the processor clear of danger, thus enabling tighter voltage margins that improve performance or lower power consumption.We describe the problem of voltage variation and the factors that influence this variation during processor design and operation. We also describe a variety of runtime hardware and software mitigation techniques that either tolerate, avoid, and/or eliminate voltage violations.We hope processor architects will find the information useful since tolerance, avoidance, and elimination are generalizable constructs that can serve as a basis for addressing other reliability challenges as well.

KEYWORDS

voltage noise, voltage smoothing, di dt , inductive noise, voltage emergencies, error detection, error correction, error recovery, transient errors, power supply noise, power delivery networks

Unintended variations in circuit lithography and undesirable fluctuations in circuit operating parameters such as supply voltage and temperature are threatening the continuation of technology scaling that microprocessor evolution relies on. Although circuit-level solutions for some variation problems may be possible, they are prohibitively expensive and impractical for commodity processors, on which not only the consumer market but also an increasing segment of the business market now depends. Solutions at the microarchitecture level and even the software level, on the other hand, overcome some of these circuitlevel challenges without significantly raising costs or lowering performance. Using examples drawn from our Alarms Project and related work, we illustrate how collaborative design that encompasses circuits, architecture, and chip-resident software leads to a cost-effective solution for inductive voltage noise, sometimes called the dI/dt problem.

The strategy that we use for assuring correctness while preserving performance can be extended to other variation problems. Index Terms—Dynamic variation, error correction, error detection, error recovery, error resiliency, hw/sw co-design, inductive noise, power supply noise, reliability, resilient design, resilient microprocessor, timing error, variation, voltage droop.

More than 20% of the available energy is lost in “the last centimeter” from the PCB board to the microprocessor chip due to inherent inefficiencies of power delivery subsystems (PDSs) in today’s computing systems. By series-stacking multiple voltage domains to eliminate explicit voltage conversion and reduce loss along the power delivery path, voltage stacking (VS) is a novel configuration that can improve power delivery efficiency (PDE). However, VS suffers from aggravated levels of supply noise caused by current imbalance between the stacking layers, preventing its practical adoption in mainstream computing systems. Throughput-centric manycore architectures such as GPUs intrinsically exhibit more balanced workloads, yet suffer from lower PDE, making them ideal platforms to implement voltage stacking. In this paper, we present a cross-layer approach to practical voltage stacking implementation in GPUs. It combines circuit-level voltage regulation using distributed charge-recycling integrated voltage regulators (CR-IVRs) with architecture-level voltage smoothing guided by control theory. Our proposed voltage-stacked GPUs can eliminate 61.5% of total PDS energy loss and achieve 92.3% system-level power delivery efficiency, a 12.3% improvement over the conventional single-layer based PDS. Compared to the circuit-only solution, the cross-layer approach significantly reduces the implementation cost of voltage stacking (88% reduction in area overhead) without compromising supply reliability under worst-case scenarios and across a wide range of real-world benchmarks. In addition, we demonstrate that the cross-layer solution not only complements on-chip CR-IVRs to transparently manage current imbalance and restore stable layer voltages, but also serves as a seamless interface to accommodate higher-level power optimization techniques, traditionally thought to be incompatible with a VS configuration.

The commoditization of hardware, data center economies of scale, and Internet-scale workload growth all demand greater power efficiency to sustain scalability. Traditional enterprise workloads, which are typically memory and I/O bound, have been well served by chip multiprocessors comprising of small, power-efficient cores. Recent advances in mobile computing have led to modern small cores capable of delivering even better power efficiency. While these cores can deliver performance-per-Watt efficiency for data center workloads, small cores impact application quality-of-service robustness, and flexibility, as these workloads increasingly invoke computationally intensive kernels. These challenges constitute the price of efficiency. We quantify efficiency for an industry-strength online web search engine in production at both the microarchitecture- and system-level, evaluating search on server and mobile-class architectures using Xeon and Atom processors.

Categories and Subject Descriptors

C.0 [Computer Systems Organization]: General—System architectures; C.4 [Computer Systems Organization]: Performance of Systems—Design studies, Reliability, availability, and serviceability

General Terms

Measurement, Experimentation, Performance

Inductive noise forces microprocessor designers to sacrifice performance in order to ensure correct and reliable operation of their designs. The possibility of wide fluctuations in supply voltage means that timing margins throughout the processor must be set pessimistically to protect against worst-case droops and surges. While sensor-based reactive schemes have been proposed to deal with voltage noise, inherent sensor delays limit their effectiveness. Instead, this paper describes a voltage emergency predictor that learns the signatures of voltage emergencies (the combinations of control flow and microarchitectural events leading up to them) and uses these signatures to prevent recurrence of the corresponding emergencies. In simulations of a representative superscalar microprocessor in which fluctuations beyond 4% of nominal voltage are treated as emergencies (an aggressive configuration), these signatures can pinpoint the likelihood of an emergency some 16 cycles ahead of time with 90% accuracy. This lead time allows machines to operate with much tighter voltage margins (4% instead of 13%) and up to 13.5% higher performance, which closely approaches the 14.2% performance improvement possible with an ideal oracle-based predictor.

Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin’s rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application’s original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin’s versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), ItaniumR , and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.

Categories and Subject Descriptors

D.2.5 [Software Engineering]: Testing and Debugging-code inspections and walk-throughs, debugging aids, tracing; D.3.4 [Programming Languages]: Processorscompilers, incremental compilers

General Terms

Languages, Performance, Experimentation

Keywords

Instrumentation, program analysis tools, dynamic compilation