With growing Field Programmable Gate Array (FPGA) device sizes and their integration in environments enabling sharing of computing resources such as cloud and edge computing, there is a requirement to share the FPGA area between multiple tasks. The resource sharing typically involves partitioning the FPGA space into fix-sized slots. This results in suboptimal resource utilisation and relatively poor

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning

This article addresses novel real-valued consensus problems in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked agents. By adopting several mobile adversary models from the computer science literature, we develop protocols which can mitigate the influence of such malicious agents. The algorithms follow the class of mean subsequence reduced

In this article we present an algorithm for a high performance, unbounded, portable, multi-producer/multi-consumer, lock-free FIFO (first-in first-out) queue. Aside from its competitive performance on current hardware, it is further characterized by its integrated memory reclamation mechanism, which is able to reliably and deterministically de-allocate nodes as soon as the final operation with a reference

Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests

Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and Identically Distributed (non-IID) user data harms the convergence speed of the FL algorithms. Furthermore, most existing work on FL measures global-model accuracy, but in many cases

A propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions is presented. Using effectively lossless compression, the simulation state is stored as a set of moments of the lattice Boltzmann distribution function, instead of the distribution function itself. An efficient cache-aware propagation pattern for this moment representation has the

Caching contents close to end-users can improve the network performance, while causing the problem of guaranteeing consistency. Specifically, solutions are classified into validation and invalidation, the latter of which can provide strong cache consistency strictly required in some scenarios. To date, little work on the analysis of cache invalidation has been covered. In this work, by using conditional

In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem ( ${{\sf ERAP}}$ ) as a Mixed-Integer Linear Program ( ${{\sf MILP}}$ ) and prove that ${{\sf ERAP}}$ is ${{\sf NP}}$ -hard. To solve the problem efficiently

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose

The increasing computational complexity of DNNs achieved unprecedented successes in various areas such as machine vision and natural language processing (NLP), e.g., the recent advanced Transformer has billions of parameters. However, as large-scale DNNs significantly exceed GPU’s physical memory limit, they cannot be trained by conventional methods such as data parallelism. Pipeline parallelism that

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading

This work provides an optimal checkpointing strategy to protect iterative applications from fail-stop errors. We consider a general framework, where the application repeats the same execution pattern by executing consecutive iterations, and where each iteration is composed of several tasks. These tasks have different execution lengths and different checkpoint costs. Assume that there are $n$ tasks

Post-quantum cryptography was proposed in the past years due to the foreseeable emergence of quantum computers that are able to break the conventional public key cryptosystems at acceptable costs. However, post-quantum schemes are usually less efficient than conventional ones, which makes them less practical in scenarios with limited resources or high concurrency. Server-side applications always feature

Parallelism patterns (e.g., map or reduce) have proven to be effective tools for parallelizing high-performance applications. In this article, we study the recursive registration of a series of electron microscopy images – a time consuming and imbalanced computation necessary for nano-scale microscopy analysis. We show that by translating the image registration into a specific instance of the prefix

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for handling power demand response to help the grid maintain stability due to more flexible workload management attributed to their distributed nature. However, they also require computing demand response to avoid overload and maintain reliability

Molecular dynamics (MD) simulations are playing an increasingly important role in many areas ranging from chemical materials to biological molecules. With the continuing development of MD models, the potentials are getting larger and more complex. In this article, we focus on the reactive force field (ReaxFF) potential from LAMMPS to optimize the computation of interactions. We present our efforts

Unexpected bursty traffic brought by certain sudden events, such as news in the spotlight on a social network or discounted items on sale, can cause severe load imbalance in backend services. Migrating hot data—the standard approach to achieve load balance—meets a challenge when handling such unexpected load imbalance, because migrating data will slow down the server that is already under heavy pressure

GPU is the dominant platform for accelerating general-purpose workloads due to its computing capacity and cost-efficiency. GPU applications cover an ever-growing range of domains. To achieve high throughput, GPUs rely on massive multi-threading and fast context switching to overlap computations with memory operations. We observe that among the diverse GPU workloads, there exists a significant class

It has become a recent trend that large volumes of data are generated, stored, and processed across geographically distributed datacenters. When popular data parallel frameworks, such as MapReduce and Spark, are employed to process such geo-distributed data, optimizing the network transfer in communication stages becomes increasingly crucial to application performance, as the inter-datacenter links

The $t/k$t/k-diagnosability and $h$h-extra connectivity are regarded as two important indicators to improve the network reliability. The $t/k$ -diagnosis strategy can significantly improve the self-diagnosing capability of a network at the expense of no more than $k$ fault-free nodes being mistakenly diagnosed as faulty. The $h$ -extra connectivity can tremendously improve the real fault tolerability

Driven by the demands of deep learning, many hardware accelerators, including GPUs, have begun to include specialized tensor processing units to accelerate matrix operations. However, general-purpose GPU applications that have little or no large dense matrix operations cannot benefit from these tensor units. This article proposes Tensorox, a framework that exploits the half-precision tensor cores available

Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition

Parallel technology boosts data processing in recent years, and parallel direct data processing on hierarchically compressed documents exhibits great promise. The high-performance direct data processing technique brings large savings in both time and space by removing the need for decompressing data. However, its benefits have been limited to data traversal operations; for random accesses, direct data

While petabytes of data are generated each day by a number of independent computing devices, only a few of them can be finally collected and used for deep learning (DL) due to the apprehension of data security and privacy leakage, thus seriously retarding the extension of DL. In such a circumstance, federated learning (FL) was proposed to perform model training by multiple clients’ combined data without

General sparse matrix-matrix multiplication (SpGEMM) is one of the most important mathematical library routines in a number of applications. In recent years, several efficient SpGEMM algorithms have been proposed, however, most of them are based on the compressed sparse row (CSR) format, and the possible performance gain from exploiting other formats has not been well studied. And some specific algorithms

Live streaming services have gained extreme popularity in recent years. Due to the spiky traffic patterns of live videos, utilizing distributed edge servers to improve viewers’ quality of experience (QoE) has become a common practice nowadays. Nevertheless, the current client-driven content caching mechanism does not support pre-caching from the cloud to the edge, resulting in a considerable amount

In many data-intensive applications, workflow is often used as an important model for organizing data processing tasks and resource provisioning is an important and challenging problem for improving the performance of workflows. Recently, system variations in the cloud and large-scale clusters, such as those in I/O and network performances and failure events, have been observed to greatly affect the

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven

Applications running on Network on Chip (NoC) based multicore systems demand increased on-chip network bandwidth that can cater to the need for intensive communication among the cores and caches. Due to strict area and power budget, the bandwidth offered by NoC is very limited. Data-intensive and communication-centric applications on encountering a cache miss lead to a considerable burden on the underlying

The adoption of heterogeneous multicore architectures into deadline-constrained embedded systems has various benefits in terms of schedulability and energy-efficiency. Existing energy-efficient algorithms, in this domain, allocate tasks to their energy-favorable core-types while using dynamic voltage and frequency scaling to reduce energy consumption. However, the practicality of such algorithms is

As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage

Software update systems must guarantee high availability, integrity and security even in presence of cyber attacks. We propose the first survivable software update framework for the secure distribution of confidential updates that is based on a distributed infrastructure with no single points of failure. Previous works guarantee either survivability or confidentiality of software updates but do not

Commercial Hardware Transactional Memory (HTM) systems are best-effort designs that leverage the coherence substrate to detect conflicts eagerly. Resolving conflicts in favor of the requesting core is the simplest option for ensuring deadlock freedom, yet it is prone to livelocks. In this work, we propose and evaluate DeTraS (Delayed Transactional Stores), an HTM-aware store buffer design aimed at

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current

How to improve the repair performance of erasure-coded storage is a critical issue for maintaining high reliability of modern large-scale storage systems. Locally repairable codes (LRC) are one popular family of repair-efficient erasure codes that mitigate the repair bandwidth and are deployed in practice. To adapt to the changing demands of access efficiency and fault tolerance, modern storage systems

Communication has been considered as a major bottleneck in large-scale decentralized training systems since participating nodes iteratively exchange large amounts of intermediate data with their neighbors. Although compression techniques like sparsification can significantly reduce the communication overhead in each iteration, errors caused by compression will be accumulated, resulting in a severely

Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners

The depthwise separable convolution is commonly seen in convolutional neural networks (CNNs), and is widely used to reduce the computation overhead of a standard multi-channel 2D convolution. Existing implementations of depthwise separable convolutions target accelerating model training with large batch sizes with a large number of samples to be processed at once. Such approaches are inadequate for

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this article

Supercomputing applications are increasingly adopting the MPI+threads programming model over the traditional “MPI everywhere” approach to better handle the disproportionate increase in the number of cores compared with other on-node resources. In practice, however, most applications observe a slower performance with MPI+threads primarily because of poor communication performance. Recent research efforts

The parallelization of Smith-Waterman (SW) sequence comparison tools for long DNA sequences has been a big challenge over the years, requesting the use of several devices and sophisticated optimizations. Pruning is one of these optimizations, which can reduce considerably the amount of computation. This article proposes MultiBP, a sequence comparison solution in multiple GPUs with block pruning. Two

Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for

Performance evaluation is a key task in computing and communication systems. Benchmarking is one of the most common techniques for evaluation purposes, where the performance of a set of representative applications is used to infer system responsiveness in a general usage scenario. Unfortunately, most benchmarking suites are limited to a reduced number of applications, and in some cases, rigid execution

While the prevalent cloud storage platforms are offering convenient services in support of diverse data-driven applications for clients, various security concerns raise in terms of data confidentiality, availability, and retrievability. Among them, servers’ dishonesty on the location-specific data backup becomes a serious concern when the data stands out clients’ control, considering the strict regulations

In the population protocol model, many problems cannot be solved in a self-stabilizing manner. However, global knowledge, such as the number of nodes in a network, sometimes enables the design of a self-stabilizing protocol for such problems. For example, it is known that we can solve the self-stabilizing leader election in complete graphs if and only if every node knows the exact number of nodes.

The advantages of the second-generation AMD EPYC Rome processors can be successfully used in the race to Exascale. However, the novel architecture’s complexity makes it challenging to adapt demanding scientific codes - like stencil ones - to platforms with Rome CPUs. This article tackles this challenge by exploring the adaptation of the stencil-based CFD (computational fluid dynamics) application called

Modeling the consumption of limited resources, e.g., time or energy, plays a central role on the design of reactive systems such as embedded controllers. To this aim, quantitative objectives are defined on game arenas that can be easily modeled as weighted graphs. Instances of these games, called energy games , can be solved in ${\mathcal {O}(\vert {E}\vert {\cdot }\vert {V}\vert {\cdot }W)}$ where

Following the trend of increasing autonomy in cyber-physical systems, parallel embedded architectures have enabled devices to better handle the large streams of data and intensive computation required by such autonomous systems. However, while the explosion of highly-parallel platforms has seen a proportional growth in the number of applications/devices that utilize these platforms, the embedded systems

The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this article, we propose ARENA – an asynchronous reconfigurable accelerator ring architecture as a potential scenario on how the future HPC and data centers will be like. Despite using the coarse-grained reconfigurable

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects

The Hadoop enabled cloud platforms are gradually becoming preferred computational environment to execute scientific big data workloads in a periodic manner. However, it is observed that the default data placement approach of such cloud platforms is not the efficient one and often ends up with significant data transfer overhead leading to degradation of the overall job completion time. In this article

The increasingly popular fused batch-streaming big data framework, Apache Flink, has many performance-critical as well as untamed configuration parameters. However, how to tune them for optimal performance has not yet been explored. Machine learning (ML) has been chosen to tune the configurations for other big data frameworks (e.g., Apache Spark), showing significant performance improvements. However

As the scientific community prepares to deploy an increasingly complex and diverse set of applications on exascale platforms, the need to assess reproducibility of simulations and identify the root causes of reproducibility failures increases correspondingly. One of the greatest challenges facing reproducibility issues at exascale is the inherent non-determinism at the level of inter-process communication

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. Existing distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may

Within scientific infrastructure scientists execute millions of computational jobs daily, resulting in the movement of petabytes of data over the heterogeneous infrastructure. Monitoring the computing and user activities over such a complex infrastructure is incredibly demanding. Whereas present solutions are traditionally based on a Relational Database Management System (RDBMS) for data storage and

Aiming at a class of high-order strict feedback nonlinear multi-agent systems with communication constraints, a novel distributed adaptive back-stepping control method is proposed to cooperatively track the moving targets. First, five agents are used as controlled objects, and all five agents form a “leader-follower” mode with a distributed control structure. Meanwhile, the leader's moving velocity

This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing