Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.

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Approximate computing: An emerging paradigm for energy-efficient design

Recent technological advances in the development of flash-memory based devices have consolidated their leadership position as the preferred storage media in the embedded systems market and opened new vistas for deployment in enterprise-scale storage systems. Unlike hard disks, flash devices are free from any mechanical moving parts, have no seek or rotational delays and consume lower power. However, the internal idiosyncrasies of flash technology make its performance highly dependent on workload characteristics. The poor performance of random writes has been a cause of major concern, which needs to be addressed to better utilize the potential of flash in enterprise-scale environments. We examine one of the important causes of this poor performance: the design of the Flash Translation Layer (FTL), which performs the virtual-to-physical address translations and hides the erase-before-write characteristics of flash. We propose a complete paradigm shift in the design of the core FTL engine from the existing techniques with our Demand-based Flash Translation Layer (DFTL), which selectively caches page-level address mappings. We develop a flash simulation framework called FlashSim. Our experimental evaluation with realistic enterprise-scale workloads endorses the utility of DFTL in enterprise-scale storage systems by demonstrating: (i) improved performance, (ii) reduced garbage collection overhead and (iii) better overload behavior compared to state-of-the-art FTL schemes. For example, a predominantly random-write dominant I/O trace from an OLTP application running at a large financial institution shows a 78% improvement in average response time (due to a 3-fold reduction in operations of the garbage collector), compared to a state-of-the-art FTL scheme. Even for the well-known read-dominant TPC-H benchmark, for which DFTL introduces additional overheads, we improve system response time by 56%.

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DFTL: a flash translation layer employing demand-based selective caching of page-level address mappings

Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this article we survey the state of the art in the design and analysis of external memory (or EM) algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs. We consider a variety of EM paradigms for solving batched and online problems efficiently in external memory. For the batched problem of sorting and related problems such as permuting and fast Fourier transform, the key paradigms include distribution and merging. The paradigm of disk striping offers an elegant way to use multiple disks in parallel. For sorting, however, disk striping can be nonoptimal with respect to I/O, so to gain further improvements we discuss distribution and merging techniques for using the disks independently. We also consider useful techniques for batched EM problems involving matrices (such as matrix multiplication and transposition), geometric data (such as finding intersections and constructing convex hulls), and graphs (such as list ranking, connected components, topological sorting, and shortest paths). In the online domain, canonical EM applications include dictionary lookup and range searching. The two important classes of indexed data structures are based upon extendible hashing and B-trees. The paradigms of filtering and bootstrapping provide a convenient means in online data structures to make effective use of the data accessed from disk. We also reexamine some of the above EM problems in slightly different settings, such as when the data items are moving, when the data items are variable-length (e.g., text strings), or when the allocated amount of internal memory can change dynamically. Programming tools and environments are available for simplifying the EM programming task. During the course of the survey, we report on some experiments in the domain of spatial databases using the TPIE system (transparent parallel I/O programming environment). The newly developed EM algorithms and data structures that incorporate the paradigms we discuss are significantly faster than methods currently used in practice.

https://users.cs.duke.edu/~reif/courses/alglectures/vitter.papers/Vit.IO_survey.pdf

External memory algorithms and data structures: dealing with massive data

2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.

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Automated control of multiple virtualized resources

Speculative adders have attracted strong interest for achieving sublogarithmic delays by exploiting the tradeoffs between correctness and performance. Speculative adders also find use in the design of error-free variable latency adders, which combine speculation with error correction to achieve high performance for low area overhead over traditional adders. This paper describes static window addition (SWA), a novel function speculation technique for the design of low overhead, high performance variable latency adders. Analytical models for the error rate of SWA-based speculative adders are developed to facilitate both design exploration and convergence. We show that on average, variable latency addition using SWA-based speculative adders is 10% faster than the fastest DesignWare adder with area requirements of -5 to 40% for different adder widths.

Static window addition: A new paradigm for the design of variable latency adders

Parallel I/O architectures are increasingly deployed for high performance computing and in shared data centers. In these environments it is desirable to provide QoS-based allocation of disk bandwidth to different applications sharing the I/O system. In this paper, we introduce a model of disk bandwidth allocation, and provide efficient scheduling algorithms to assign the bandwidth among the concurrent applications.

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Scheduling with QoS in parallel I/O systems

The paper examines the problem of fair bandwidth allocation in heterogeneous storage systems in the framework of multi-resource allocation. We first extend the Bottleneck Aware Allocation model recently proposed by the authors to directly compute the maximum allocation satisfyinglocal fairness, envy freedom and sharing incentive. Next, we broaden the solution space to all allocations that satisfy envy freedom and sharing incentive even if they do not satisfy local fairness. We present an efficient algorithm to maximize the system utilization in the more general model.

Fairness-efficiency tradeoffs in tiered storage allocation

We investigate the problem of smoothing multiplexed network traffic when either a streaming server transmits data to multiple clients or a storage server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. Possible applications include improvement in the bandwidth utilization of network links and reduction in the energy consumption of server hosts. In the data transmission problem, we consider the case in which the clients have different buffer capacities and unlimited bandwidth constraints or unlimited buffer capacities and different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule. By provably minimizing the variance of the required aggregate bandwidth, lexicographically optimal smoothing makes the maximum resource requirements within the network more predictable and increases the useful resource utilization. It also improves fairness in sharing a network link among multiple users and makes new requests from future clients more likely to be successfully admitted without the need for rescheduling previously accepted traffic. With appropriate hardware and system support, data traffic smoothing can also reduce the energy consumption of the host processor and the communication links. Overall, we expect that efficient resource management at the network edges will better meet quality of service requirements without restricting the scalability of the system.

Optimal lexicographic shaping of aggregate streaming data

A primary reason for the susceptibility of systolic algorithms to faults is their strong dependence on the interconnection between the processors in a systolic array. A technique to transform any linear systolic algorithm into an equivalent pipelined algorithm that executes on arbitrary trees is presented. 5 references.

Peter Varman

Papers

Static window addition: A new paradigm for the design of variable latency adders

Scheduling with QoS in parallel I/O systems

Fairness-efficiency tradeoffs in tiered storage allocation

Optimal lexicographic shaping of aggregate streaming data

Design of robust systolic algorithms