bluuebunny/crossref_metadata_2025_split huggingface dataset:
The liquid-argon scintillation pulseshape in DEAP-3600
Authors:   DEAP Collaboration, Adhikari P. , Ajaj R. , Araujo G. R. , Batygov M. , Beltran B. , Bina C. E. , Boulay M. G. , Broerman B. , Bueno J. F. , Butcher A. , Cai B. , Cárdenas-Montes M. , Cavuoti S. , Chen Y. , Cleveland B. T. , Corning J. M. , Daugherty S. J. , Di Stefano P. , Dering K. , Doria L. , Duncan F. A. , Dunford M. , Erlandson A. , Fatemighomi N. , Fiorillo G. , Flower A. , Ford R. J. , Gagnon R. , Gallacher D. , Garcés E. A. , Abia P. García , Garg S. , Giampa P. , Goeldi D. , Golovko V. V. , Gorel P. , Graham K. , Grant D. R. , Grobov A. , Hallin A. L. , Hamstra M. , Harvey P. J. , Hearns C. , Ilyasov A. , Joy A. , Jillings C. J. , Kamaev O. , Kaur G. , Kemp A. , Kochanek I. , Kuźniak M. , Langrock S. , La Zia F. , Lehnert B. , Levashko N. , Li X. , Litvinov O. , Lock J. , Longo G. , Machulin I. , Majewski P. , McDonald A. B. , McElroy T. , McGinn T. , McLaughlin J. B. , Mehdiyev R. , Mielnichuk C. , Monroe J. , Nadeau P. , Nantais C. , Ng C. , Noble A. J. , Oliviéro G. , Ouellet C. , Pal S. , Pasuthip P. , Peeters S. J. M. , Pesudo V. , Piro M.-C. , Pollmann T. R. , Rand E. T. , Rethmeier C. , Retière F. , García E. Sanchez , Sánchez-Pastor T. , Santorelli R. , Seeburn N. , Skensved P. , Smith B. , Smith N. J. T. , Sonley T. , Stainforth R. , Stone C. , Strickland V. , Stringer M. , Sur B. , Vázquez-Jáuregui E. , Veloce L. , Viel S. , Walding J. , Waqar M. , Ward M. , Westerdale S. , Willis J. , Zuñiga-Reyes A. 
Published: April 2020

AbstractDEAP-3600 is a liquid-argon scintillation detector looking for dark matter. Scintillation events in the liquid argon (LAr) are registered by 255 photomultiplier tubes (PMTs), and pulseshape discrimination (PSD) is used to suppress electromagnetic background events. The excellent PSD performance of LAr makes it a viable target for dark matter searches, and the LAr scintillation pulseshape discussed here is the basis of PSD. The observed pulseshape is a combination of LAr scintillation physics with detector effects. We present a model for the pulseshape of electromagnetic background events in the energy region of interest for dark matter searches. The model is composed of (a) LAr scintillation physics, including the so-called intermediate component, (b) the time response of the TPB wavelength shifter, including delayed TPB emission at $${\mathcal {O}}$$O(ms) time-scales, and c) PMT response. TPB is the wavelength shifter of choice in most LAr detectors. We find that approximately 10% of the intensity of the wavelength-shifted light is in a long-lived state of TPB. This causes light from an event to spill into subsequent events to an extent not usually accounted for in the design and data analysis of LAr-based detectors.
Efficient memory shadowing for 64-bit architectures
Authors: Zhao Qin , Bruening Derek , Amarasinghe Saman 
Published: April 2020

Shadow memory is used by dynamic program analysis tools to store metadata for tracking properties of application memory. The efficiency of mapping between application memory and shadow memory has substantial impact on the overall performance of such analysis tools. However, traditional memory mapping schemes that work well on 32-bit architectures cannot easily port to 64-bit architectures due to the much larger 64-bit address space.
This paper presents EMS64, an efficient memory shadowing scheme for 64-bit architectures. By taking advantage of application reference locality and unused regions in the 64-bit address space, EMS64 provides a fast and flexible memory mapping scheme without relying on any underlying platform features or requiring any specific shadow memory size. Our experiments show that EMS64 is able to reduce the runtime shadow memory translation overhead to 81% on average, which almost halves the overhead of the fastest 64-bit shadow memory system we are aware of.
Patterns as objects in grace
Authors: Homer Michael , Noble James , Bruce Kim B. , Black Andrew P. , Pearce David J. 
Published: April 2020

Object orientation and pattern matching are often seen as conflicting approaches to program design. Object-oriented programs place type-dependent behavior inside objects and invoke it via dynamic dispatch, while pattern-matching programs place type-dependent behavior outside data structures and invoke it via multiway conditionals (case statements).
Grace is a new, dynamic, object-oriented language designed to support teaching: to this end, Grace needs to support both styles. We explain how this conflict can be resolved gracefully: by modelling patterns and cases as partial functions, reifying those functions as objects, and then building up complex patterns from simpler ones using pattern combinators. We describe the implementation of this design as an object-oriented framework, and a case study of its effectiveness.
Paragon
Authors: Delimitrou Christina , Kozyrakis Christos 
Published: April 2020

Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications.
We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state.
We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.
DeAliaser
Authors: Ahn Wonsun , Duan Yuelu , Torrellas Josep 
Published: April 2020

Alias analysis is a critical component in many compiler optimizations. A promising approach to reduce the complexity of alias analysis is to use speculation. The approach consists of performing optimizations assuming the alias relationships that are true most of the time, and repairing the code when such relationships are found not to hold through runtime checks.
This paper proposes a general alias speculation scheme that leverages upcoming hardware support for transactions with the help of some ISA extensions. The ability of transactions to checkpoint and roll back frees the compiler to pursue aggressive optimizations without having to worry about recovery code. Also, exposing the memory conflict detection hardware in transactions to software allows runtime checking of aliases with little or no overhead. We test the potential of the novel alias speculation approach with Loop Invariant Code Motion (LICM), Global Value Numbering (GVN), and Partial Redundancy Elimination (PRE) optimization passes. On average, they are shown to reduce program execution time by 9% in SPEC FP2006 applications and 3% in SPEC INT2006 applications over the alias analysis of a state-of-the-art compiler.
STABILIZER
Authors: Curtsinger Charlie , Berger Emery D. 
Published: April 2020

Researchers and software developers require effective performance evaluation. Researchers must evaluate optimizations or measure overhead. Software developers use automatic performance regression tests to discover when changes improve or degrade performance. The standard methodology is to compare execution times before and after applying changes.
Unfortunately, modern architectural features make this approach unsound. Statistically sound evaluation requires multiple samples to test whether one can or cannot (with high confidence) reject the null hypothesis that results are the same before and after. However, caches and branch predictors make performance dependent on machine-specific parameters and the exact layout of code, stack frames, and heap objects. A single binary constitutes just one sample from the space of program layouts, regardless of the number of runs. Since compiler optimizations and code changes also alter layout, it is currently impossible to distinguish the impact of an optimization from that of its layout effects.
This paper presents Stabilizer, a system that enables the use of the powerful statistical techniques required for sound performance evaluation on modern architectures. Stabilizer forces executions to sample the space of memory configurations by repeatedly re-randomizing layouts of code, stack, and heap objects at runtime. Stabilizer thus makes it possible to control for layout effects. Re-randomization also ensures that layout effects follow a Gaussian distribution, enabling the use of statistical tests like ANOVA. We demonstrate Stabilizer's efficiency (<7% median overhead) and its effectiveness by evaluating the impact of LLVM's optimizations on the SPEC CPU2006 benchmark suite. We find that, while -O2 has a significant impact relative to -O1, the performance impact of -O3 over -O2 optimizations is indistinguishable from random noise.
Verifying security invariants in ExpressOS
Authors: Mai Haohui , Pek Edgar , Xue Hui , King Samuel Talmadge , Madhusudan Parthasarathy 
Published: April 2020

Security for applications running on mobile devices is important. In this paper we present ExpressOS, a new OS for enabling high-assurance applications to run on commodity mobile devices securely. Our main contributions are a new OS architecture and our use of formal methods for proving key security invariants about our implementation. In our use of formal methods, we focus solely on proving that our OS implements our security invariants correctly, rather than striving for full functional correctness, requiring significantly less verification effort while still proving the security relevant aspects of our system.
We built ExpressOS, analyzed its security, and tested its performance. Our evaluation shows that the performance of ExpressOS is comparable to an Android-based system. In one test, we ran the same web browser on ExpressOS and on an Android-based system, and found that ExpressOS adds 16% overhead on average to the page load latency time for nine popular web sites.
Efficient virtualization on embedded power architecture® platforms
Authors: Mittal Aashish , Bansal Dushyant , Bansal Sorav , Sethi Varun 
Published: April 2020

Power Architecture® processors are popular and widespread on embedded systems, and such platforms are increasingly being used to run virtual machines. While the Power Architecture meets the Popek-and-Goldberg virtualization requirements for traditional trap-and-emulate style virtualization, the performance overhead of virtualization remains high. For example, workloads exhibiting a large amount of kernel activity typically show 3-5x slowdowns over bare-metal.
Recent additions to the Linux kernel contain guest and host side paravirtual extensions for Power Architecture platforms. While these extensions improve performance significantly, they are guest-specific, guest-intrusive, and cover only a subset of all possible virtualization optimizations.
We present a set of host-side optimizations that achieve comparable performance to the aforementioned paravirtual extensions, on an unmodified guest. Our optimizations are based on adaptive in-place binary translation. Unlike the paravirtual approach, our solution is guest neutral. We implement our ideas in a prototype based on Qemu/KVM. After our modifications, KVM can boot an unmodified Linux guest around 2.5x faster. We contrast our optimization approach with previous similar binary translation based approaches for the x86 architecture; in our experience, each architecture presents a unique set of challenges and optimization opportunities.
Fine-grained fault tolerance using device checkpoints
Authors: Kadav Asim , Renzelmann Matthew J. , Swift Michael M. 
Published: April 2020

Recovering faults in drivers is difficult compared to other code because their state is spread across both memory and a device. Existing driver fault-tolerance mechanisms either restart the driver and discard its state, which can break applications, or require an extensive logging mechanism to replay requests and recreate driver state. Even logging may be insufficient, though, if the semantics of requests are ambiguous. In addition, these systems either require large subsystems that must be kept up-to-date as the kernel changes, or require substantial rewriting of drivers.
We present a new driver fault-tolerance mechanism that provides fine-grained control over the code protected. Fine-Grained Fault Tolerance (FGFT) isolates driver code at the granularity of a single entry point. It executes driver code as a transaction, allowing roll back if the driver fails. We develop a novel checkpointing mechanism to save and restore device state using existing power management code. Unlike past systems, FGFT can be incrementally deployed in a single driver without the need for a large kernel subsystem, but at the cost of small modifications to the driver.
In the evaluation, we show that FGFT can have almost zero runtime cost in many cases, and that checkpoint-based recovery can reduce the duration of a failure by 79% compared to restarting the driver. Finally, we show that applying FGFT to a driver requires little effort, and the majority of drivers in common classes already contain the power-management code needed for checkpoint/restore.
Volition
Authors: Qian Xuehai , Torrellas Josep , Sahelices Benjamin , Qian Depei 
Published: April 2020

Sequential Consistency (SC) is the most intuitive memory model, and SC Violations (SCVs) produce unintuitive, typically incorrect executions. Most prior SCV detection schemes have used data races as proxies for SCVs, which is highly imprecise. Other schemes that have targeted data-race cycles are either too conservative or are designed only for two-processor cycles and snoopy-based systems.
This paper presents Volition, the first hardware scheme that detects SCVs in a relaxed-consistency machine precisely, in a scalable manner, and for an arbitrary number of processors in the cycle. Volition leverages cache coherence protocol transactions to dynamically detect cycles in memory-access orders across threads. When a cycle is about to occur, an exception is triggered. Volition can be used in both directory- and snoopy-based coherence protocols. Our simulations of Volition in a 64-processor multicore with directory-based coherence running SPLASH-2 and Parsec programs shows that Volition induces negligible traffic and execution overhead. In addition, it can detect SCVs with several processors. Volition is suitable for on-the-fly use.
Topological transformation approaches to optimizing TCAM-based packet classification systems
Authors: Meiners Chad R. , Liu Alex X. , Torng Eric 
Published: April 2020

Several range reencoding schemes have been proposed to mitigate the effect of range expansion and the limitations of small capacity, large power consumption, and high heat generation of TCAM-based packet classification systems. However, they all disregard the semantics of classifiers and therefore miss significant opportunities for space compression.
In this paper, we propose new approaches to range reencoding by taking into account classifier semantics. Fundamentally different from prior work, we view reencoding as a topological transformation process from one colored hyperrectangle to another where the color is the decision associated with a given packet. We present two orthogonal, yet composable, reencoding approaches, domain compression and prefix alignment. Our techniques significantly outperform all previous reencoding techniques. In comparison with the state-of-the-art results, our experimental results show that our techniques achieve at least 7 times more space reduction in terms of TCAM space for an encoded classifier and at least 3 times more space reduction in terms of TCAM space for a reencoded classifier and its transformers.
Network adiabatic theorem
Authors: Rajagopalan Shreevatsa , Shah Devavrat , Shin Jinwoo 
Published: April 2020

The popularity ofAloha-like algorithms for resolution of contention between multiple entities accessing common resources is due to their extreme simplicity and distributed nature. Example applications of such algorithms include Ethernet and recently emerging wireless multi-access networks. Despite a long and exciting history of more than four decades, the question of designing an algorithm that isessentiallyas simple and distributed as Aloha while being efficient has remained unresolved.In this paper, we resolve this question successfully for a network of queues where contention is modeled through independent-set constraints over the network graph. The work by Tassiulas and Ephremides (1992) suggests that an algorithm that schedules queues so that the summation of `weight' of scheduled queues is maximized, subject to constraints, is efficient. However, implementing such an algorithm using Aloha-like mechanism has remained a mystery. We design such an algorithm building upon a Metropolis-Hastings sampling mechanism along with selection of `weight' as an appropriate function of the queue-size. The key ingredient in establishing the efficiency of the algorithm is a noveladiabatic-like theorem for the underlying queueing network, which may be of general interest in the context of dynamical systems.
Optimal power allocation in server farms
Authors: Gandhi Anshul , Harchol-Balter Mor , Das Rajarshi , Lefurgy Charles 
Published: April 2020

Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance.
By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter.
We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.
Leveraging discarded samples for tighter estimation of multiple-set aggregates
Authors: Cohen Edith , Kaplan Haim 
Published: April 2020

Many datasets, including market basket data, text or hypertext documents, and events recorded in different locations or time periods, can be modeled as a collection of sets over a ground set of keys. Common queries over such data, including similarity or association rules are represented as the weight or selectivity of keys that satisfy some selection predicate defined over keys' attributes and memberships in particular sets.
On massive data sets, exact computation of such aggregates can be inefficient or infeasible, and therefore, approximate queries are processed over sketches of the sets. Sketches based on coordinated random samples are scalable and flexible and well suited for many applications. Queries are resolved by producing a sketch of the union of sets used in the predicate from the sketches of these sets and then applying an estimator to this union-sketch.

We derive novel tighter (unbiased) estimators that leverage sampled keys that are present in the union of applicable sketches but excluded from the union sketch. We establish analytically that our estimators dominate estimators applied to the union-sketch for
all queries and data sets
. Empirical evaluation on synthetic and real data reveals that on typical applications we can expect a 25% 4 fold reduction in estimation error.
Modeling channel popularity dynamics in a large IPTV system
Authors: Qiu Tongqing , Ge Zihui , Lee Seungjoon , Wang Jia , Zhao Qi , Xu Jun 
Published: April 2020

Understanding the channel popularity or content popularity is an important step in the workload characterization for modern information distribution systems (e.g., World Wide Web, peer-to-peer file-sharing systems, video-on-demand systems).
In this paper, we focus on analyzing the channel popularity in the context of Internet Protocol Television (IPTV). In particular, we aim at capturing two important aspects of channel popularity - the distribution and temporal dynamics of the channel popularity. We conduct in-depth analysis on channel popularity on a large collection of user channel access data from a nation-wide commercial IPTV network. Based on the findings in our analysis, we choose a stochastic model that finds good matches in all attributes of interest with respect to the channel popularity. Furthermore, we propose a method to identify subsets of user population with inherently different channel interest.
By tracking the change of population mixtures among different user classes, we extend our model to a multi-class population model, which enables us to capture the moderate diurnal popularity patterns exhibited in some channels. We also validate our channel popularity model using real user channel access data from commercial IPTV network.