Latency (engineering)

About: Latency (engineering) is a research topic. Over the lifetime, 7278 publications have been published within this topic receiving 115409 citations. The topic is also known as: lag.

The Remarkable Stability of the Latent Reservoir for HIV-1 in Resting Memory CD4+ T Cells

Abstract: The modern era of antiretroviral therapy (ART) for human immunodeficiency virus type 1 (HIV-1) infectionbegan in the mid-1990s with the introduction of 2 new classes of antiretroviral drugs, the protease inhibitors (PIs) and the nonnucleoside reverse-transcriptase inhibitors. Combinations consisting of 1 of these drugs along with 2 nucleoside analogue reverse-transcriptase inhibitors rapidly reduced plasma HIV-1 RNA levels to below the limit of detection of clinical assays [1, 2], leading to predictions that continued treatment for 2–3 years could cure the infection [3]. Although it did not prove curative, combination ART became the mainstay of HIV treatment, allowing durable control of viral replication and reversal or prevention of immunodeficiency [4]. A major reason why ART did not prove curative is the persistence of a latent form of the virus in a small population of resting memory CD4 T cells [5, 6]. In these cells, the viral genome is stably integrated into host cell DNA, but viral genes are not expressed at significant levels in part because of the absence of key host transcription factors that are recruited to the HIV prompter only after T-cell activation. The latent reservoir for HIV-1 was originally demonstrated using an assay in which resting cells from patients are activated to reverse latency [6]. Viruses released from individual latently infected cells are expanded in culture. This viral outgrowth assay (VOA) was used to demonstrate the remarkable stability of the latent reservoir [7–9]. The half-life of this pool of cells was shown to be 44 months. At this rate of decay, >70 years would be required for a pool of just 10 cells to decay completely [8, 9]. Initial studies of the decay of the latent reservoir were completed in 2003 [9]. Since that time, remarkable advances in ART have taken place, including the introduction of new classes of antiretroviral drugs, such as integrase inhibitors, and the development of simplified regimens in which multiple antiretroviral drugs are combined into a single pill that can be taken once daily [4]. In this context, an extensive and careful study of latent reservoir decay by Crooks et al [10], reported in this issue of the Journal, is of particular interest. The authors have reexamined the stability of the latent reservoir using longitudinal VOAs in a series of 37 patients, some of whom have been receiving treatment for most of the modern ART era. Despite the long duration of treatment in some patients and the changes in ART, the authors found that the decay rate of the latent reservoir is almost exactly the same as that reported in 2003. The half-life measured by Crooks et al is 43 months [10]. The fact that the decay rate measured in the present study is no different from that measured more than a decade ago confirms that the stability of the latent reservoir is not determined by treatment regimens. As long as the regimen produces a complete or near-complete arrest of new infection events, the decay of the reservoir is determined by the biology of the resting memory T cells that harbor persistent HIV-1. Pharmacodynamic studies indicate that the nonnucleoside reversetranscriptase inhibitors and PIs possess a remarkable potential to inhibit viral replication, a property that reflects an unexpected degree of cooperativity in their dose-response curves [11, 12]. At clinical concentrations, the best PIs can actually produce a 10 billion–fold inhibition of a single round of HIV-1 replication. Thus, even the early combination therapy regimens may have produced complete or near-complete inhibition of new infection events in drug-adherent patients. Subsequent improvements in ART have largely affected tolerability and convenience. Viewed in this light, the finding that the reservoir decay is constant is not surprising. The cures now being routinely achieved with direct-acting antiviral drugs Received and accepted 6 April 2015; electronically published 15 April 2015. Correspondence: Janet M. Siliciano, PhD, Johns Hopkins School of Medicine, 733 N Broadway, Miller Research Bldg 871, Baltimore, MD 21205 (jsilicia@jhmi.edu). The Journal of Infectious Diseases 2015;212:1345–7 © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals. permissions@oup.com. DOI: 10.1093/infdis/jiv219

How unfair is optimal routing

Abstract: We are given a network and a rate of traffic between a source node and a destination node, and seek an assignment of traffic to source-destination paths. We assume that each network user controls a negligible fraction of the overall traffic, so that feasible assignments of traffic to paths in the network can be modeled as network flows. We also assume that the time needed to traverse a single link of the network is load-dependent, that is, the common latency suffered by all traffic on the link increases as the link becomes more congested.We consider two types of traffic assignments. In the first, we measure the quality of an assignment by the total latency incurred by network users; an optimal assignment is a feasible assignment that minimizes the total latency. On the other hand, it is often difficult in practice to impose optimal routing strategies on the traffic in a network, leaving network users free to act according to their own interests. We assume that, in the absence of network regulation, users act in a selfish manner. Under this assumption, we can expect network traffic to converge to the second type of assignment that we consider, an assignment at Nash equilibrium. An assignment is at Nash equilibrium if no network user has an incentive to switch paths; this occurs when all traffic travels on minimum-latency paths.The following question motivates our work: is the optimal assignment really a "better" assignment than an assignment at Nash equilibrium? While the optimal assignment obviously dominates one at Nash equilibrium from the viewpoint of total latency, it may lack desirable fairness properties. For example, consider a network consisting of two nodes, s and t, and two edges, e1 and e2, from s to t. Suppose further that one unit of traffic wishes to travel from s to t, that the latency of edge e1 is always 2(1 - e) (independent of the edge congestion, where e > 0 is a very small number), and that the latency of edge e2 is the same as the edge congestion (i.e., if x units of traffic are on edge e2, then all of this flow incurs x units of latency). In the assignment at Nash equilibrium, all traffic is on the second link; in the minimum-latency assignment, 1 - e units of traffic use edge e2 while the remaining e units of traffic use edge e1. Roughly, a small fraction of the traffic is sacrificed to the slower edge because it improves the overall social welfare (by reducing the congestion experienced by the overwhelming majority of network users); needless to say, these martyrs may not appreciate a doubling of their travel time in the name of "the greater good"! Indeed, this drawback of routing traffic optimally has inspired practitioners to find traffic assignments that minimize total latency subject to explicit length constraints [1], which require that no network user experiences much more latency than in an assignment at Nash equilibrium. The central question of this paper is how much worse off can network users be in an optimal assignment than in one at Nash equilibrium? After reviewing some technical preliminaries in the next section (all of which are classical; see [2] for historical references), we provide an exact solution to this problem under weak hypotheses on the class of allowable latency functions.

μLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization

Abstract: Emerging mobile services heavily utilize Neural Networks (NNs) to improve user experiences. Such NN-assisted services depend on fast NN execution for high responsiveness, demanding mobile devices to minimize the NN execution latency by efficiently utilizing their underlying hardware resources. To better utilize the resources, existing mobile NN frameworks either employ various CPU-friendly optimizations (e.g., vectorization, quantization) or exploit data parallelism using heterogeneous processors such as GPUs and DSPs. However, their performance is still bounded by the performance of the single target processor, so that realtime services such as voice-driven search often fail to react to user requests in time. It is obvious that this problem will become more serious with the introduction of more demanding NN-assisted services. In this paper, we propose μLayer, a low latency on-device inference runtime which significantly improves the latency of NN-assisted services. μLayer accelerates each NN layer by simultaneously utilizing diverse heterogeneous processors on a mobile device and by performing computations using processor-friendly quantization. Two key findings motivate our work: 1) the existing frameworks are limited by single-processor performance as they execute an NN layer using only a single processor, and 2) the CPU and the GPU on the same mobile device achieve comparable computational throughput, making cooperative acceleration highly promising. First, to accelerate an NN layer using both the CPU and the GPU at the same time, μLayer employs a layer distribution mechanism which completely removes redundant computations between the processors. Next, μLayer optimizes the per-processor performance by making the processors utilize different data types that maximize their utilization. In addition, to minimize potential latency increases due to overly aggressive workload distribution, μLayer selectively increases the distribution granularity to divergent layer paths. Our experiments using representative NNs and mobile devices show that μLayer significantly improves the speed and the energy efficiency of on-device inference by up to 69.6% and 58.1%, respectively, over the state-of-the-art NN execution mechanism.

Effect of Increasing System Latency on Localization of Virtual Sounds

Abstract: In a virtual acoustic environment, the total system latency (TSL) refers to the time elapsed from the transduction of an event or action, such as movement of the head, until the consequences of that action cause the equivalent change in the virtual sound source. This paper reports on the impact of increasing TSL on localization accuracy when head motion is enabled. Five subjects estimated the location of 12 virtual sound sources (individualized head-related transfer functions) with latencies of 33.8, 100.4, 250.4 or 500.3 ms in an absolute judgement paradigm. Subjects also rated the perceived latency on each trial. The data indicated that localization was generally accurate, even with a latency as great as 500 ms. In particular, front-back confusions were minimal and unaffected by latency. Mean latency ratings indicated that latency had to be at least 250 ms to be readily perceived. The fact that accuracy was generally comparable for the shortest and longest latencies suggests that listeners are able to ignore latency during active localization, even though delays of this magnitude produce an obvious spatial “slewing” of the source such that it is no longer stabilized in space.

Analysis of techniques to improve protocol processing latency

Abstract: This paper describes several techniques designed to improve protocol latency, and reports on their effectiveness when measured on a modern RISC machine employing the DEC Alpha processor. We found that the memory system---which has long been known to dominate network throughput---is also a key factor in protocol latency. As a result, improving instruction cache effectiveness can greatly reduce protocol processing overheads. An important metric in this context is the memory cycles per instructions (mCPI), which is the average number of cycles that an instruction stalls waiting for a memory access to complete. The techniques presented in this paper reduce the mCPI by a factor of 1.35 to 5.8. In analyzing the effectiveness of the techniques, we also present a detailed study of the protocol processing behavior of two protocol stacks---TCP/IP and RPC---on a modern RISC processor.