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

Nowadays, we are witnessing the diffusion of Stream Processing Systems (SPSs) able to analyze data streams in near realtime. Traditional SPSs like Storm and Flink target distributed clusters and adopt the continuous streaming model , where inputs are processed as soon as they are available while outputs are continuously emitted. Recently, there has been a great focus on SPSs for scale-up machines.

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article

The parallel computing capabilities of GPUs have a significant impact on computationally intensive iterative tasks. Offloading part or all of the deep learning tasks from the CPU to the GPU for execution is mainstream. However, a large number of redundant iterative calculations exist in the iterative process of computing tasks. Therefore, we propose a GPU-based distributed incremental iterative computing

In cloud computing, service providers rent heterogeneous servers from cloud providers, i.e., Infrastructure as a Service (IaaS), to meet requests of consumers. The heterogeneity of servers and impatience of consumers pose great challenges to service providers for profit maximization. In this article, we transform this problem into a multi-queue model where the optimal expected response time of each

Edit distance is one of the most fundamental problems in combinatorial optimization to measure the similarity between strings. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks

Sparse Triangular Solve (SpTRSV) is an important and extensively used kernel in scientific computing. Parallelism within SpTRSV depends upon matrix sparsity pattern and, in many cases, is non-uniform from one computational step to the next. In cases where the SpTRSV computational steps have contrasting parallelism characteristics– some steps are more parallel, others more sequential in nature, the

With this special section we bring you a practice and experience effort in transparency and reproducibility for large-scale computational science. A unique section, it consists of a research work plus six critques, each by a student team that reproduced the work. The original research work has been expanded in its science and also in its contribution to open science with a discussion of the student

Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow, as it can contribute more than 50 percent of the job completion time. While

Data centers, the critical infrastructure underpinning Cloud computing, often employ Software-Defined Networks (SDN) to manage cluster, wide-area and enterprise networks. As the network forwarding in SDN is dynamically programmed by controllers, it is crucial to ensure that the controller intent is correctly translated into underlying forwarding rules. Therefore, detecting and locating forwarding anomalies

State access in existing distributed stream processing systems is restricted locally within each operator. However, in advanced stream analytics such as online learning and dynamic graph analytics, enabling state sharing across different operators makes application development easier and stream processing more efficient. In addition, when stream records are timestamped, proper time semantics should

Multi-party set reconciliation is a key component in distributed and networking systems. It naturally contains two dimensions, i.e., set representation and reconciliation protocol. However, existing sketch data structures are insufficient to satisfy the new needs brought by the multi-party scenario simultaneously, including space-efficiency, mergeability, and completeness. The current reconciliation

Many deep learning applications deployed in dynamic environments change over time, in which the training models are supposed to be continuously updated with streaming data to guarantee better descriptions of data trends. However, most state-of-the-art learning frameworks support well in offline training methods while omitting online model updating strategies. In this work, we propose and implement

Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS resource consumption is correctly accounted); 3) fair

Despite great advancements in hardware-assisted virtualization of the x86 architecture, certain workloads still suffer significant overhead. This article dissects said overhead in the context of multi-threading. We describe the state-of-the-art, pinpoint challenges, and suggest improvements, aiming to provide a valuable reference to developers and users of virtualization systems alike. We study the

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions show downsides in communication and storage scalability, as well as decentralization. In this article, we propose LightChain , which is the first blockchain architecture that operates over a Distributed Hash Table

Decentralized dual methods play significant roles in large-scale optimization, which effectively resolve many constrained optimization problems in machine learning and power systems. In this article, we focus on studying a class of totally non-smooth constrained composite optimization problems over multi-agent systems, where the mutual goal of agents in the system is to optimize a sum of two separable

Presents the introductory editorial for this issue of the publication.

Deep Neural Networks (DNNs) have emerged as an important class of machine learning algorithms, providing accurate solutions to a broad range of applications. Sparsity in activation maps in DNN training presents an opportunity to reduce computations. However, exploiting activation sparsity presents two major challenges: i) profiling activation sparsity during training comes with significant overhead

Modern computer systems are increasingly bounded by the available or permissible power at multiple layers from components to systems. To cope with this reality, it is necessary to understand how power bounds impact the design and performance of emergent computer systems. Prior work mainly focuses on power capping and budgeting on individual components without coordinating them to achieve the best possible

Graph partitioning is a mandatory step in large-scale distributed graph processing. When partitioning real-world power-law graphs, the edge partitioning algorithm performs better than the traditional vertex partitioning algorithm, because it can cut a single vertex into multiple replicas to apportion the computation. Many advanced edge partitioning methods are designed for partitioning a static graph

In the IoT-based systems, the fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to reduce the response delay. However, achieving such a benefit is still challenging in the systems with high rate of requests, which imply long queues of tasks in the fog nodes, thus exposing probably an inefficiency

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement

Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertex features from

GPU performance of the lattice Boltzmann method (LBM) depends heavily on memory access patterns. When implemented with GPUs on complex domains, typically, geometric data is accessed indirectly and lattice data is accessed lexicographically. Although there are a variety of other options, no study has examined the relative efficacy between them. Here, we examine a suite of memory access schemes via empirical

In recent years, increased I/O demand of Virtual Machines (VMs) in large-scale data centers and cloud computing has encouraged system architects to design high-performance storage systems. One common approach to improving performance is to employ fast storage devices such as Solid-State Drives (SSDs) as an I/O caching layer for slower storage devices. SSDs provide high performance, especially on random

This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle

Given a social network modeled as a weighted graph $G$ , the influence maximization problem seeks $k$ vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable

Matrix factorization on sparse matrices has been proven to be an effective approach for data mining and machine learning. However, the prior parallel implementations for matrix factorization fail to capture the internal social property embedded in real-world use cases. This article presents an efficient implementation of the alternative least squares (ALS) algorithm called BALS built on top of a new

FPGA accelerators are gaining increasing attention in both cloud and edge computing because of their hardware flexibility, high computational throughput, and low power consumption. However, the design flow of FPGAs often requires specific knowledge of the underlying hardware, which hinders the wide adoption of FPGAs by application developers. Therefore, the virtualization of FPGAs becomes extremely

Deep learning (DL) models are increasingly built on federated edge participants holding local data. To enable insight extractions without the risk of information leakage, DL training is usually combined with differential privacy (DP). The core theme is to tradeoff learning accuracy by adding statistically calibrated noises, particularly to local gradients of edge learners, during model training. However

Consistent hashing has played an indispensable role in cloud infrastructure, although its load balancing performance is not necessarily perfect. Consistent hashing has long remained the most widely used method despite many methods being proposed to improve load balancing because these methods trade off load balancing against consistency, memory usage, lookup performance, and/or fault-tolerance. This

Machine learning is widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation

The accelerated digitalisation of society along with technological evolution have extended the geographical span of cyber-physical systems. Two main threats have made the reliable and real-time control of these systems challenging: (i) uncertainty in the communication infrastructure induced by scale, and heterogeneity of the environment and devices; and (ii) targeted attacks maliciously worsening the

Scalability of distributed deep learning (DL) training with parameter server (PS) architecture is often communication constrained in large clusters. There are recent efforts that use a layer by layer strategy to overlap gradient communication with backward computation so as to reduce the impact of communication constraint on the scalability. However, the approaches could bring significant overhead

The substitution of nucleotides at specific positions in the genome of a population, known as single-nucleotide polymorphisms (SNPs), has been correlated with a number of important diseases. Complex conditions such as Alzheimer's disease or Crohn's disease are significantly linked to genetics when the impact of multiple SNPs is considered. SNPs often interact in an epistatic manner, where the joint

The booming of Convolutional Neural Networks (CNNs) has empowered lots of computer-vision applications. Due to its stringent requirement for computing resources, substantial research has been conducted on how to optimize its deployment and execution on resource-constrained devices. However, previous works have several weaknesses, including limited support for various CNN structures, fixed scheduling

Under-allocating or over-allocating computation resources (e.g., CPU cores) can prolong the completion time of data-parallel jobs in a distributed system. We present a predictor, ReLocag, to find the near-optimal number of CPU cores to minimize job completion time (JCT). ReLocag includes a graph convolutional network (GCN) and a fully-connected network (FCNN). The GCN learns the dependency between

Network Function Virtualization (NFV) offers service delivery flexibility and reduces overall costs by running service function chains (SFCs) on commodity servers with many cores. Existing solutions for placing SFCs in one server treat all CPU cores as equal and allocate isolated CPU cores to network functions (NFs). However, advanced servers often adopt Non-Uniform Memory Access (NUMA) architecture

With the advancement of edge computing and network function virtualization, it is promising to provide flexible and low-latency network services at the network edge. However, due to resource limitation and heterogeneity of servers at the edge, it is unlikely to achieve an efficient service function chain deployment without considering the resource management of edge servers jointly. In this article

We present a scalable high-performance computing implementation of an agent-based economic model using distributed + shared-memory hybrid parallelization paradigms, capable of simulating 1:1 scale models of large economies like the eurozone. Agent-based economic models consist of millions of agents interacting over several graphs, which are either centralized or scale-free in nature. While most of

Choosing the best cloud configuration for large-scale data analytics jobs deployed in the cloud can substantially improve their performance and reduce costs. However, current cloud providers offer a wide variety of instance types and customized cluster sizes, making it both time-consuming and costly to pinpoint the optimal cloud configuration. This article presents the design, implementation, and evaluation

Asynchronous iterative computations (AIC) are common in machine learning and data mining systems. However, the lack of synchronization barriers in asynchronous processing brings challenges for continuous processing while workers might fail. There is no global synchronization point that all workers can roll back to. In this article, we propose a fault-tolerant framework for asynchronous iterative computations

Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless

Container-based operation system (OS) level virtualization has been adopted by many edge-computing platforms. However, for an edge server, inter-container communications, and container management consume significant CPU resources. Given an application composed of interdependent tasks, the number of such operations is closely related to the dependency between the scheduled tasks. Thus, to improve the

Voluminous, time-series data streams originating in continuous sensing environments pose data ingestion and processing challenges. We present a holistic methodology centered around data sketching to address both challenges. We introduce an order-preserving sketching algorithm that we have designed for space-efficient representation of multi-feature streams with native support for stream processing

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks (DNNs) on computer clusters. With the increase of computational power, network communications generally limit the system scalability. Wait-free backpropagation (WFBP) is a popular solution to overlap communications with computations during the training process. In this article, we observe that many