Parallel GPGPU applications rely on barrier synchronization to align thread block activity. Few prior work has studied and characterized barrier synchronization within a thread block and its impact on performance. In this paper, we find that barriers cause substantial stall cycles in barrier-intensive GPGPU applications although GPGPUs employ lightweight hardware-support barriers. To help investigate the reasons, we define the execution between two adjacent barriers of a thread block as a warp-phase. We find that the execution progress within a warp-phase varies dramatically across warps, which we call warp-phase-divergence. While warp-phasedivergence may result from execution time disparity among warps due to differences in application code or input, and/or shared resource contention, we also pinpoint that warp-phase-divergence may result from warp scheduling.
To mitigate barrier induced stall cycle inefficiency, we propose barrier-aware warp scheduling (BAWS). It combines two techniques to improve the performance of barrier-intensive GPGPU applications. The first technique, most-waiting-first (MWF), assigns a higher scheduling priority to the warps of a thread block that has a larger number of warps waiting at a barrier. The second technique, critical-fetch-first (CFF), fetches instructions from the warp to be issued by MWF in the next cycle. To evaluate the efficiency of BAWS, we consider 13 barrier-intensive GPGPU applications, and we report that BAWS speeds up performance by 17% and 9% on average (and up to 35% and 30%) over loosely-round-robin (LRR) and greedy-then-oldest (GTO) warp scheduling, respectively. We compare BAWS against recent concurrent work SAWS, finding that BAWS outperforms SAWS by 7% on average and up to 27%. For non-barrier-intensive workloads, we demonstrate that BAWS is performance-neutral compared to GTO and SAWS, while improving performance by 5.7% on average (and up to 22%) compared to LRR. BAWS’ hardware co
Web computing is gradually shifting toward mobile devices, in which the energy budget is severely constrained. As a result, Web developers must be conscious of energy efficiency. However, current Web languages provide developers little control over energy consumption. In this paper, we take a first step toward language-level research to enable energy-efficient Web computing. Our key motivation is that mobile systems can wisely budget energy usage if informed with user quality-of-service (QoS) constraints. To do this, programmers need new abstractions. We propose two language abstractions, QoS type and QoS target, to capture two fundamental aspects of user QoS experience. We then present GreenWeb, a set of language extensions that empower developers to easily express the QoS abstractions as program annotations. As a proof of concept, we develop a GreenWeb runtime, which intelligently determines how to deliver specified user QoS expectation while minimizing energy consumption. Overall, GreenWeb shows significant energy savings (29.2% ⇠ 66.0%) over Android’s default Interactive governor with few QoS violations. Our work demonstrates a promising first step toward language innovations for energy-efficient Web computing. Categories and Subject Descriptors D.3.2 [Programming Language]: Language Classifications–Specialized application languages; D.3.3 [Programming Language]: Language Constructs and Features–Constraints Keywords Energy-efficiency, Web, Mobile computing
Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy systems, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a small and battery-efficient line of defense requires a rigorous foundation for evaluating HMDs. We introduce Sherlock—a white-box methodology that quantifies an HMD’s ability to detect malware and identify the reason why. Sherlock first deconstructs malware into atomic, orthogonal actions to synthesize a diverse malware suite. Sherlock then drives both malware and benign programs with real user-inputs, and compares their executions to determine an HMD’s operating range, i.e., the smallest malware actions an HMD can detect. We show three case studies using Sherlock to not only quantify HMDs’ operating ranges but design better detectors. First, using information about concrete malware actions, we build a discretewavelet transform based unsupervised HMD that outperforms prior work based on power transforms by 24.7% (AUC metric). Second, training a supervised HMD using Sherlock’s diverse malware dataset yields 12.5% better HMDs than past approaches that train on ad-hoc subsets of malware. Finally, Sherlock shows why a malware instance is detectable. This yields a surprising new result—obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.
We introduce a system-level Simulation and Analysis Engine (SAE) framework based on dynamic binary instrumentation for fine-grained and customizable instruction-level introspection of everything that executes on the processor. SAE can instrument the BIOS, kernel, drivers, and user processes. It can also instrument multiple systems simultaneously using a single instrumentation interface, which is essential for studying scale-out applications. SAE is an x86 instruction set simulator designed specifically to enable rapid prototyping, evaluation, and validation of architectural extensions and program analysis tools using its flexible APIs. It is fast enough to execute full platform workloads—a modern operating system can boot in a few minutes—thus enabling research, evaluation, and validation of complex functionalities related to multicore configurations, virtualization, security, and more. To reach high speeds, SAE couples tightly with a virtual platform and employs both a just-in-time (JIT) compiler that helps simulate simple instructions eciently and a fast interpreter for simulating new or complex instructions. We describe SAE’s architecture and instrumentation engine design and show the framework’s usefulness for single- and multi-system architectural and program analysis studies.
Temperature inversion is a transistor-level effect that can improve performance when temperature increases. It has largely been ignored in the past because it does not occur in the typical operating region of a processor, but temperature inversion is becoming increasing important in current and future technologies. In this paper, we study temperature inversion’s implications on architecture design, and power and performance management. We present the first public comprehensive measurement-based analysis on the effects of temperature inversion on a real processor, using the AMD A10- 8700P processor as our system under test. We show that the extra timing margin introduced by temperature inversion can provide more than 5% Vdd reduction benefit, and this improvement increases to more than 8% when operating in the near-threshold, low-voltage region. To harness this opportunity, we present Tistates, a power management technique that sets the processor’s voltage based on real-time silicon temperature to improve power efficiency. Ti-states lead to 6% to 12% measured power saving across a range of different temperatures compared to a fixed margin. As technology scales to FD-SOI and FinFET, we show there is an ideal operating temperature for various workloads to maximize the benefits of temperature inversion. The key is to counterbalance leakage power increase at higher temperatures with dynamic power reduction by the Ti-states. The projected optimal temperature is typically around 60°C and yields 8% to 9% chip power saving. The optimal high-temperature can be exploited to reduce design cost and runtime operating power for overall cooling. Our findings are important for power and thermal management in future chips and process technologies.
Keywords-timing margin; temperature inversion; power management; reliability; technology scaling
The traditional guardbanding approach to ensure processor reliability is becoming obsolete because it always over-provisions voltage and wastes a lot of energy. As a next-generation alternative, adaptive guardbanding dynamically adjusts chip clock frequency and voltage based on timing margin measured at runtime. With adaptive guardbanding, voltage guardband is only provided when needed, thereby promising significant energy eciency improvement. In this paper, we provide the first full-system analysis of adaptive guardbanding’s implications using a POWER7+ multicore. On the basis of a broad collection of hardware measurements, we show the benefits of adaptive guardbanding in a practical setting are strongly dependent upon workload characteristics and chip-wide multicore activity. A key finding is that adaptive guardbanding’s benefits diminish as the number of active cores increases, and they are highly dependent upon the workload running. Through a series of analysis, we show these high-level system e↵ects are the result of interactions between the application characteristics, architecture and the underlying voltage regulator module’s loadline e↵ect and IR drop e↵ects. To that end, we introduce adaptive guardband scheduling to reclaim adaptive guardbanding’s e- ciency under di↵erent enterprise scenarios. Our solution reduces processor power consumption by 6.2% over a highly optimized system, e↵ectively doubling adaptive guardbanding’s original improvement. Our solution also avoids malicious workload mappings to guarantee application QoS in the face of adaptive guardbanding hardware’s variable performance.
Mobile Web applications have become an integral part of our society. They pose a high demand for application quality of service (QoS). However, the energy-constrained nature of mobile devices makes optimizing for QoS difficult. Prior art on energy efficiency optimizations has only focused on the trade-off between raw performance and energy consumption, ignoring the application QoS characteristics. In this paper, we propose the concept of energy-efficient QoS (eQoS) to capture the trade-off between QoS and energy consumption. Given the fundamental event-driven nature of mobile Web applications, we further propose event-based scheduling as an optimization framework for eQoS. The event-based scheduling automatically reasons about users’ QoS requirements, and accurately slacks the events’ execution time to save energy without violating end users’ experience. We demonstrate a working prototype using the Google Chromium and V8 framework on the Samsung Exynos 5410 SoC (used in the Galaxy S4 smartphone). Based on real hardware and software measurements, we achieve 41.2% energy saving with only 0.4% of QoS violations perceptible to end users.
Energy efficiency is undoubtedly important for GPU architectures. Besides the traditionally explored energy-efficiency optimization techniques, exploiting the supply voltage guardband remains a promising yet unexplored opportunity. Our hardware measurements show that up to 23% of the nominal supply voltage can be eliminated to improve GPU energy efficiency by as much as 25%. The key obstacle for exploiting this opportunity lies in understanding the characteristics and root causes of large voltage droops in GPU architectures and subsequently smoothing them away without severe performance penalties. The GPU’s manycore nature complicates the voltage noise phenomenon, and its distinctive architecture features from the CPU necessitate a GPU-specific voltage noise analysis. In this paper, we make the following contributions. First, we provide a voltage noise categorization framework to identify, characterize, and understand voltage noise in the manycore GPU architecture. Second, we perform a microarchitecture-level voltage-droop root-cause analysis for the two major droop types we identify, namely the local first-order droop and the global second-order droop. Third, on the basis of our categorization and characterization, we propose a hierarchical voltage smoothing mechanism that mitigates each type of voltage droop. Our evaluation shows it can reduce up to 31% worst-case droop, which translates to 11.8% core-level and 7.8% processor-level energy reduction
Server-side Web applications are in the midst of a software evolution. Application developers are turning away from the established thread-per-request model, where each request gets a dedicated thread on the server, and toward event-driven programming platforms, which promise improved scalability and CPU utilization . In response, we perform a microarchitectural analysis of these applications in current server processors and identify several serious performance bottlenecks and optimization opportunities for future processor designs.
In contrast to traditional computing systems, such as desktops and servers, that are programmed to perform “compute-bound” and “run-to-completion” tasks, mobile applications are designed for user interactivity. Factoring user interactivity into computer system design and evaluation is important, yet possesses many challenges. In particular, systematically studying interactive mobile applications across the diverse set of mobile devices available today is difficult due to the mobile device fragmentation problem. At the time of writing, there are 18,796 distinct Android mobile devices on the market and will only continue to increase in the future. Differences in screen sizes, resolutions and operating systems impose different interactivity requirements, making it difficult to uniformly study these systems. We present Mosaic, a cross-platform, timing-accurate record and replay tool for Android-based mobile devices. Mosaic overcomes device fragmentation through a novel virtual screen abstraction. User interactions are translated from a physical device into a platform-agnostic intermediate representation before translation to a target system. The intermediate representation is human-readable, which allows Mosaic users to modify previously recorded traces or even synthesize their own user interactive sessions from scratch. We demonstrate that Mosaic allows user interaction traces to be recorded on emulators, smartphones, tablets, and development boards and replayed on other devices. Using Mosaic we were able to replay 45 different Google Play applications across multiple devices, and also show that we can perform cross-platform performance comparisons between two different processors under identical user interactions.
Energy eciency of GPU architectures has emerged as an important aspect of computer system design. In this paper, we explore the energy benefits of reducing the GPU chip’s voltage to the safe limit, i.e. Vmin point. We perform such a study on several commercial o↵- the-shelf GPU cards. We find that there exists about 20% voltage guardband on those GPUs spanning two architectural generations, which, if “eliminated” completely, can result in up to 25% energy savings on one of the studied GPU cards. The exact improvement magnitude depends on the program’s available guardband, because our measurement results unveil a program dependent Vmin behavior across the studied programs. We make fundamental observations about the programdependent Vmin behavior. We experimentally determine that the voltage noise has a larger impact on Vmin compared to the process and temperature variation, and the activities during the kernel execution cause large voltage droops. From these findings, we show how to use a kernel’s microarchitectural performance counters to predict its Vmin value accurately. The average and maximum prediction errors are 0.5% and 3%, respectively. The accurate Vmin prediction opens up new possibilities of a cross-layer dynamic guardbanding scheme for GPUs, in which software predicts and manages the voltage guardband, while the functional correctness is ensured by a hardware safety net mechanism.
This work proposes an empirical Bias Temperature Instability (BTI) stress-relaxation model based on the superposition property. The model was used to study the instantaneous frequency fluctuation in a fast Dynamic Voltage and Frequency Scaling (DVFS) environment. VDD and operating frequency information for this study were collected from an ARM Cortex A15 processor based development board running an Android operating system. Simulation results show that the frequency peaks and dips are functions of mainly two parameters: (1) the amount of stress or recovery experienced by the circuit prior to the VDD switching and (2) the frequency sensitivity to device aging after the VDD switching.
Voltage noise is a major obstacle in improving processor energy eciency because it necessitates large operating voltage guardbands that increase overall power consumption and limit peak performance. Identifying the leading root causes of voltage noise is essential to minimize the unnecessary guardband and maximize the overall energy eciency. We provide the first-ever modeling and characterization of voltage noise in GPUs based on a new simulation infrastructure called GPUVolt. Using it, we identify the key intracore microarchitectural components (e.g., the register file and special functional units) that significantly impact the GPU’s voltage noise. We also demonstrate that intercore-aligned microarchitectural activity detrimentally impacts the chipwide worst-case voltage droops. On the basis of these findings, we propose a combined register-file and execution-unit throttling mechanism that smooths GPU voltage noise and reduces the guardband requirement by as much as 29%.
Categories and Subject Descriptors
C.4 [Performance of Systems]: Modeling techniques, Reliability, availability, and serviceability
General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and performance per watt has emerged as a more crucial evaluation metric than peak performance. As such, GPU architects require robust tools that will enable them to quickly explore new ways to optimize GPGPUs for energy efficiency. We propose a new GPGPU power model that is configurable, capable of cycle-level calculations, and carefully validated against real hardware measurements. To achieve configurability, we use a bottom-up methodology and abstract parameters from the microarchitectural components as the model’s inputs. We developed a rigorous suite of 80 microbenchmarks that we use to bound any modeling uncertainties and inaccuracies. The power model is comprehensively validated against measurements of two commercially available GPUs, and the measured error is within 9.9% and 13.4% for the two target GPUs (GTX 480 and Quadro FX5600). The model also accurately tracks the power consumption trend over time. We integrated the power model with the cycle-level simulator GPGPU-Sim and demonstrate the energy savings by utilizing dynamic voltage and frequency scaling (DVFS) and clock gating. Traditional DVFS reduces GPU energy consumption by 14.4% by leveraging within-kernel runtime variations. More finer-grained SM cluster-level DVFS improves the energy savings from 6.6% to 13.6% for those benchmarks that show clustered execution behavior. We also show that clock gating inactive lanes during divergence reduces dynamic power by 11.2%.
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.
We describe and evaluate HELIX, a new technique for automatic loop parallelization that assigns successive iterations of a loop to separate threads. We show that the inter-thread communication costs forced by loop-carried data dependences can be mitigated by code optimization, by using an effective heuristic for selecting loops to parallelize, and by using helper threads to prefetch synchronization signals. We have implemented HELIX as part of an optimizing compiler framework that automatically selects and parallelizes loops from general sequential programs. The framework uses an analytical model of loop speedups, combined with profile data, to choose loops to parallelize. On a six-core Intel✌R Core❚▼ i7-980X, HELIX achieves speedups averaging 2.25✂, with a maximum of 4.12✂, for thirteen C benchmarks from SPEC CPU2000.