Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporating Probabilistic Optimizations for Resource Provisioning of Data Processing Workflows

Workflow is an important model for big data processing and resource provisioning is crucial to the performance of workflows. Recently, system variations in the cloud and large-scale clusters, such as those in I/O and network performances, have been observed to greatly affect the performance of workflows. Traditional resource provisioning methods, which overlook these variations, can lead to suboptimal resource provisioning results. In this paper, we provide a general solution for workflow performance optimizations considering system variations. Specifically, we model system variations as time-dependent random variables and take their probability distributions as optimization input. Despite its effectiveness, this solution involves heavy computation overhead. Thus, we propose three pruning techniques to simplify workflow structure and reduce the probability evaluation overhead. We implement our techniques in a runtime library, which allows users to incorporate efficient probabilistic optimization into existing resource provisioning methods. Experiments show that probabilistic solutions can improve the performance by 51% compared to state-of-the-art static solutions while guaranteeing budget constraint, and our pruning techniques can greatly reduce the overhead of probabilistic optimization.
TEA: A Traffic-efficient Erasure-coded Archival Scheme for In-memory Stores

To achieve good trade-off between access performance and memory efficiency, it is appropriate to adopt replication and erasure coding to keep popular and unpopular in-memory datasets, respectively. An issue of redundancy transition from replication to erasure coding (a.k.a., erasure-coded archival) should be addressed for unpopular in-memory datasets, since caching workloads exhibit long-tail distributions and most in-memory data are unpopular. In this paper, we propose an encoding-oriented replica placement policy - ERP - by incorporating an interleaved declustering mechanism, and design a traffic-efficient erasure-coded archival schemes - TEA - for ERP-powered in-memory stores. With ERP in place, TEA embraces three salient features: (i) it alleviates cross-rack traffic raised by retrieving data-block replicas, (ii) it improves rack-level load balancing by distributing replicas via load-aware primary-rack-selection approach, and (iii) it mitigates block-relocation operations launched to sustain rack-level fault-tolerance. The empirical results show that TEA not only brings forth lower cross-rack traffic than four candidate encoding schemes, but also exhibits superb archival-throughput and rack-level-balancing performance. In particular, TEA accelerates archival throughput by at least 70.8%; and improves rack-level load-balancing by a factor of more than 1.58x relative to the four competitors.
OSP: Overlapping Computation and Communication in Parameter Server for Fast Machine Learning

When running in Parameter Server (PS), the Distributed Stochastic Gradient Descent (SGD) incurs significant communication delays because after pushing their updates, computing nodes (workers) have to wait for the global model to be communicated back from the master in every iteration. In this paper, we devise a new synchronization parallel mechanism named overlap synchronization parallel (OSP), in which the waiting time is removed by conducting computation and communication in an overlapped manner. We theoretically prove that our mechanism could achieve the same convergence rate compared to the sequential SGD for non-convex problems. Evaluations show that our mechanism significantly improves performance over the state-of-the-art ones, e.g., by 4× for both AlexNet and ResNet18 in terms of convergence speed.
Position-dependent arrays and their application for high performance code generation

Modern parallel hardware promises unprecedented performance, for the gifted few experts who can program it correctly. Code generators from high-level languages provide an attractive alternative, promising to deliver high performance automatically. Existing projects such as Accelerate, Futhark, Halide, or Lift show that this approach is feasible. Unfortunately, existing efforts focus on computations over tensors: regularly shaped higher dimensional arrays. This limits the expressiveness of these approaches and excludes many interesting data structures that are commonly encoded manually in memory, such as trees or triangular matrices. This paper presents an extended array type that lifts this restriction. For multidimensional arrays, the size of a nested array might depend on its position in the surrounding arrays, enabling the expression of computations over less regularly shaped data structures. However, position-dependent arrays bring new challenges for high-performance code generation, as indexing elements in memory becomes more challenging. This paper shows how these challenges are addressed by extending the existing Lift type system and compiler. The experimental results show that this approach enables the efficient code generation of triangular matrix-vector multiplication, with performance improvements over cuBLAS on an Nvidia GPU by up to 2×. Furthermore, we show a use case for a low-level optimization for avoiding unnecessary out-of-bound checks in stencils, leading to up to 3× improvements over already optimized generated stencil codes.
swATOP: Automatically Optimizing Deep Learning Operators on SW26010 Many-Core Processor

Achieving an optimized mapping of Deep Learning (DL) operators to new hardware architectures is the key to building a scalable DL system. However, handcrafted optimization involves huge engineering efforts, due to the variety of DL operator implementations and complex programming skills. Targeting the innovative many-core processor SW26010 adopted by the 3rd fastest supercomputer Sunway TaihuLight, an end-to-end automated framework called swATOP is presented as a more practical solution for DL operator optimization. Arithmetic intensive DL operators are expressed into an auto-tuning-friendly form, which is based on tensorized primitives. By describing the algorithm of a DL operator using our domain specific language (DSL), swATOP is able to derive and produce an optimal implementation by separating hardware-dependent optimization and hardware-agnostic optimization. Hardware-dependent optimization is encapsulated in a set of tensorized primitives with sufficient utilization of the underlying hardware features. The hardware-agnostic optimization contains a scheduler, an intermediate representation (IR) optimizer, an auto-tuner, and a code generator. These modules cooperate to perform an automatic design space exploration, to apply a set of programming techniques, to discover a near-optimal solution, and to generate the executable code. Our experiments show that swATOP is able to bring significant performance improvement on DL operators in over 88% of cases, compared with the best-handcrafted optimization. Compared to a black-box autotuner, the tuning and code generation time can be reduced to minutes from days using swATOP.
PCC2018-bot: A Telegram Bot for 'Palermo Capitale della Cultura 2018' Events Powered by Linked Ppen Data and Microdata Annotations

This paper describes a practice for live social reuse of Schema.org annotations and Linked Open Data in the realm of events. The events of the “ Palermo Capitale della Cultura 2018” initiative of the Ministry of Culture of Italy, were semantically enhanced by interlinking the available open data with related information inferred from the Linked Open Data cloud (namely DBpe-dia and Geonames). The resulting dataset - stored as an RDF graph within a tri-plestore was exposed via API in a knowledge graph by using the Schema.org vocabulary . The dataset was made also accessible to an event search assistant for tourists implemented as a bot for the instant messaging application Tele-gram. This effort shows how plain open metadata can be powered by Linked Data and semantic vocabularies like Schema.org, to became rich machine-understandable descriptions usable by automatic bots to provide improved question answering experiences for the social user.
A Cognitive Architecture for Ambient Intelligence Systems

Nowadays, the use of intelligent systems in homes and workplaces is a well-established reality. Research efforts are moving towards increasingly complex Ambient Intelligence (AmI) systems that exploit a wide variety of sensors, software modules and stand-alone systems. Unfortunately, using more data often comes at a cost, both in energy and computational terms. Finding the right trade-off between energy savings, information costs and accuracy of results is a major challenge, especially when trying to integrate many heterogeneous modules. Our approach fits into this scenario by proposing an ontology-based AmI system with a cognitive architecture, able to perceive the state of the surrounding environment, to reason on the current situation and act accordingly to modify the state of the environment based on the user’s preferences.
gemOS: Bridging the Gap between Architecture and Operating System in Computer System Education

Providing adequate exposure to architecture and OS interfaces can enable the students with better understanding of the concepts and increase their interest towards research problems crossing the hardware-software boundaries. Moreover, a framework to explore possible enhancements spanning across the architecture and operating system (OS) layers can be very useful for researchers. The existing tools and techniques used to teach system courses like OS and Computer Architecture serve the objective of respective courses to a large extent. However, exploration crossing the hardware and OS boundaries is particularly difficult due to lack of proper teaching infrastructure integrating the two layers. In this paper, we propose to use a specialized OS executing on the Gem5 full system simulator as an alternate approach to explore the architecture and OS boundaries. Open source OSes like Linux are not suitable for this purpose simply because they are not designed to be used as teaching infrastructure, especially on a system simulator like Gem5. We propose gemOS, a simple OS for 64-bit X86 simulation platform provided by the Gem5 architectural simulator. Further, we propose a simple framework using gemOS along with Gem5 and show its utility towards understanding the OS and architecture interactions, and, show the quick prototyping support of the framework to implement innovative ideas spanning across the two layers. We present use-cases and examples to demonstrate the utility of the proposed framework as a teaching and research infrastructure.
Efficient and Scalable Topic Model Training on Distributed Data-Parallel Platform

Distributed Collapsed Gibbs Sampling (CGS) in Latent Dirichlet Allocation (LDA) training usually prefers a “customized” design with sophisticated asynchronization support. However, with both algorithm level innovation and system level optimizations, we demonstrate that the “generalized” design on distributed dataparallel platform can even outperform the dedicated designs. We first present a novel CGS sampling algorithm, ZenLDA, that has different formula decomposition with different performance-accuracy tradeoff with other CGS algorithms. With respect to parallelization, we convert the serial CGS algorithm to Monte Carlo ExpectationMaximization (MCEM) algorithm thus can be parallelized in a fully batched and synchronized way. To push the performance to the limit, we also present two approximations, sparse model initialization and “converged” token exclusion, as well as several system level optimizations. Training corpus is represented as a directed graph and model parameters are annotated as the corresponding vertex attributes, thus we implemented ZenLDA and other well-known CGS algorithms on GraphX in Spark, and it has been deployed and daily used in production. We evaluated the efficiency of presented techniques against multiple datesets including web-scale corpus. Experimental results indicate that MCEM variant achieves much faster than CGS algorithms but still converges with similar accuracy, and ZenLDA is the best performer. When compared with state-of-art systems, ZenLDA achieves comparable (even better) performance with similar accuracy. Besides, ZenLDA demonstrates good scalability when dealing with large topics and huge corpus.
COMBFT: Conflicting-Order-Match based Byzantine Fault Tolerance Protocol with High Efficiency and Robustness

Byzantine Fault-Tolerant (BFT) state machine replication protocol is an important building block for highly available distributed computing. This paper presents COMBFT, a BFT protocol that achieves both efficiency and robustness simultaneously. The major novelty of COMBFT lies in Conflicting-Order-Match (COM), a new request ordering mechanism that uses a new way to select the available sequence number for requests, and detects the possible malicious primary early. COM assigns sequence number based on request interference, and requires both primary and backup nodes to conduct request ordering, which can greatly reduce the impact of malicious primary and clients. When the backup suspects the primary may be malicious, it triggers an efficient commit protocol with two phases (i.e., suspect phase and commit phase) to further confirm whether the primary is malicious, and commit the request. The performance of COMBFT is evaluated via simulations and the results illustrate the outstanding performance of COMBFT in terms of throughput, latency and fault scalability.
Multi-stage programs in context

Cross-stage persistence is an essential aspect of multi-stage programming that allows a value defined in one stage to be available in another. However, difficulty arises when implicit information held in types, type classes and implicit parameters needs to be persisted. Without a careful treatment of such implicit information---which are pervasive in Haskell---subtle yet avoidable bugs lurk beneath the surface. This paper demonstrates that in multi-stage programming care must be taken when representing quoted terms so that important implicit information is kept in context and not discarded. The approach is formalised with a type-system, and an implementation in GHC is presented that fixes problems of the previous incarnation.
Consensus-Based Attitude Maneuver of Multi-spacecraft with Exclusion Constraints

Some space missions involve cooperative multi-vehicle teams, for such purposes as interferometry and optimal sensor coverage, for example, NASA Terrestrial Planet Finder Mission. Cooperative navigation introduces extra constraints of exclusion zones between the spacecraft to protect them from damaging each other. This is in addition to external exclusion constraints introduced by damaging or blinding celestial objects. This work presents a quaternion-based attitude consensus protocol, using the communication topology of the team of spacecraft. The resulting distributed Laplacians of their communication graph are applied by semidefinite programming (SDP), to synthesize a series of time-varying optimal stochastic matrices. The matrices are used to generate various cooperative attitude maneuvers from the initial attitudes of the spacecraft. Exclusion constraints are satisfied by quaternion-based quadratically constrained attitude control (Q-CAC), where both static and dynamic exclusion zones are identified every time step, expressed as time-varying linear matrix inequalities (LMI) and solved by semidefinite programming.
Artemis: A Practical Low-latency Naming and Routing System

Today, Internet service deployment is typically implemented with server replication at multiple locations for the purpose of load balancing, failure tolerance, and user experience optimization. Domain name system (DNS) is responsible for translating human-readable domain names into network-routable IP addresses. When multiple replicas exist, upon the arrival of a query, DNS selects one replica and responds with its IP address. Thus, the delay caused by the process of DNS query including the selection of replica is part of the connection setup latency. In this paper, we proposed Artemis, a practical low-latency naming and routing system that aims at reducing the connection setup latency by eliminating the DNS query latency while keeping the ability to perform optimal server (replica) selection based on user-defined rules. Artemis achieves these goals by integrating name resolution into the transport layer handshake. Artemis allows clients to calculate locally the IP address of a Service Dispatcher, which serves as a proxy of hosting servers. Service Dispatchers forward the handshake request from a client to a server, and the response is embedded with the server's IP address back to the client. This enables clients to connect directly with servers afterward without querying DNS servers, and therefore eliminates the DNS query latency. Meanwhile, Artemis supports user-defined replica selection policies. We have implemented Artemis and evaluated its performance using the PlanetLab testbed and RIPE Atlas probes. Our results show that Artemis reduces the connection setup latency by 26.2% on average compared with the state-of-the-art.
Multi-Objective Reinforcement Learning for Reconfiguring Data Stream Analytics on Edge Computing

There is increasing demand for handling massive amounts of data in a timely manner via Distributed Stream Processing (DSP). A DSP application is often structured as a directed graph whose vertices are operators that perform transformations over the incoming data and edges representing the data streams between operators. DSP applications are traditionally deployed on the Cloud in order to explore the virtually unlimited number of resources. Edge computing has emerged as a suitable paradigm for executing parts of DSP applications by offloading certain operators from the Cloud and placing them close to where the data is generated, hence minimising the overall time required to process data events (i.e., the end-to-end latency). The operator reconfiguration consists of changing the initial placement by reassigning operators to different devices given target performance metrics. In this work, we model the operator reconfiguration as a Reinforcement Learning (RL) problem and define a multi-objective reward considering metrics regarding operator reconfiguration, and infrastructure and application improvement. Experimental results show that reconfiguration algorithms that minimise only end-to-end processing latency can have a substantial impact on WAN traffic and communication cost. The results also demonstrate that when reconfiguring operators, RL algorithms improve by over 50% the performance of the initial placement provided by state-of-the-art approaches.
A Network-aware and Partition-based Resource Management Scheme for Data Stream Processing

With the increasing demand for data-driven decision making, there is an urgent need for processing geographically distributed data streams in real-time. The existing scheduling and resource management schemes efficiently optimize stream processing performance with the awareness of resource, quality-of-service, and network traffic. However, the correlation between network delay and inter-operator communication pattern is not well-understood. In this study, we propose a network-aware and partition-based resource management scheme to deal with the ever-changing network condition and data communication in stream processing. The proposed approach applies operator fusion by considering the computational demand of individual operators and the inter-operator communication patterns. It maps the fused operators to the clustered hosts with the weighted shortest processing time heuristic. Meanwhile, we established a 3-dimensional coordinate system for prompt reflection of the network condition, real-time traffic, and resource availability. We evaluated the proposed approach against two benchmarks, and the results demonstrate the efficiency in throughput and resource utilization. We also conducted a case study and implemented a prototype system supported by the proposed approach that aims to utilize the stream processing paradigm for pedestrian behavior analysis. The prototype application estimates walking time for a given path according to the real crowd traffic. The promising evaluation results of processing performance further illustrate the efficiency of the proposed approach.
Title Influencing the World Versus Adjusting to Constraints : Social Class Moderates Responses to Discrimination Permalink

Although higher social class carries mental and physical health benefits, these advantages are less robust among members of racial and ethnic minority groups than among European Americans. We explore whether differential reactions to discrimination may be a factor in explaining why. Working-class and middle-class Latino American women engaged in an evaluative interaction with a European American woman who rejected them and held either prejudiced or unprejudiced attitudes. We examined how participants responded to this rejection by measuring neuroendocrine reactivity, executive functioning, and the affective content of their verbal responses during the interaction. Among middle-class Latinas, rejection from a prejudiced, compared to unprejudiced, out-group member was associated with less adaptive stress responses, greater cognitive depletion, and more feelings of uncertainty. In contrast, among working-class Latinas, neuroendocrine, cognitive, and affective responses were similar across the two sources of rejection. Results suggest that social class is an important moderator of responses to discrimination.
Seasonal variation in water buffaloes’ diet grazing in wet grasslands in Northern Greece

Abstract Seasonal variability in grasslands’ vegetation affects animals’ diet selection. We studied the seasonal changes in water buffaloes’ diet during grazing in wet grasslands in Northern Greece. We recorded each month the plant species of the vegetation and the species consumed by buffaloes. We categorized the former into three functional groups (graminoids, legumes, and forbs) and the latter into five groups (graminoids, legumes, forbs, shrubs, and trees). There were significant differences in the proportions (%) of functional groups in the vegetation and in buffaloes’ diet between seasons (χ2 test, P<0.05). Graminoids, legumes, and forbs participated in buffaloes’ diet in all seasons, while the less preferred functional groups were trees and shrubs. Buffaloes consumed ten species in all seasons but we observed the most diverse diet, in terms of plant species, in spring. The most frequently consumed species in each functional group were Cynodon dactylon (graminoids), Trifolium repens (legumes), Cichorium intybus (forbs), Rubus sp. (shrubs), and Populus sp. (trees). However, the majority of plant species in buffaloes’ diet was in very low proportions (<1%), while buffaloes did not sample at all 38 herbaceous species. Researchers need to conduct further research to understand water buffaloes’ foraging strategy regarding plants’ anti-quality characteristics.
How do you smile along a path?

Abstract This paper argues that a core component of root meaning is the distinction between body parts versus the body conceived as a whole. This distinction is shown to be relevant in the acceptability of motion sentences in English with whole-body roots like dance$\sqrt {\textsc{dance}} $ and body-part roots like smile$\sqrt {\textsc{smile}} $. In keeping with the assumption that roots lack syntactic category, I argue that verbal roots occur freely in syntactic structures but that some root-structure combinations are degraded (or unacceptable), and that this is due to an incompatibility between conceptual root content and interpreted syntactic structure.