CPU Efficiency Research Articles

A Comparative Study of CPU and GPU Power Consumption while using Open-Source and Proprietary Media Players

This study presents a comparative analysis of power consumption between open-source and proprietary media players when playing open-media format videos (.webm). As media consumption grows, energy-efficient software is critical for both environmental sustainability and device performance. Using tools like HWiNFO, key metrics such as GPU and CPU power consumption, memory usage, and efficiency were evaluated for popular open-source (e.g., VLC, Kodi) and proprietary (e.g., GOM Player, KMPlayer) players. The results reveal that open-source players generally consume less GPU power but more CPU resources, while proprietary players balance CPU and GPU usage with higher memory demands. The findings suggest that careful selection of media players can lead to significant energy savings over time, offering insights for developers and users focused on energy-efficient computing.

Implementation of concurrency control mechanisms to enhance the performance of multi-threaded applications

Concurrency control is a crucial aspect of multi-threaded application development, directly impacting performance and resource utilization.This research project investigates the impact of various concurrency control mechanisms on the performance of multi-threaded applications, specifically focusing on mutexes, semaphores, and lock-free data structures implemented using queues. Conducted in a Python environment within Google Colab, the study aims to assess performance metrics such as execution time, cpu usage, and memory usage. Each mechanism’s performance was evaluated through repeated trials, and the results were aggregated and averaged to ensure reliability. By systematically evaluating these concurrency control mechanisms, the project seeks to understand how each approach affects the efficiency of applications in a multi-threaded context. The findings reveal that the queue mechanism provides the fastest execution time, the semaphore mechanism, on the other hand, exhibited the lowest CPU usage, and the mutex mechanism offered a balanced performance in both speed and CPU efficiency. Memory usage was similar in mutex and semaphore, but queue differed significantly from the other two. These results show that the choice of concurrency control mechanism should be guided by the specific requirements of the application, considering factors such as workload nature and system architecture.

Accuracy and efficiency of finite element head models: The role of finite element formulation and material laws.

Traumatic brain injury is a significant problem worldwide. In the United States of America, around 1.7 million cases are documented annually, displaying the need for a deeper understanding of the effects on the human brain. The tests required for this assessment are very complex. Tests on cadavers may raise serious ethical questions, and in vivo crash tests are not viable. In this context, there is a great need to developing finite element head models (FEHM) to study the biomechanics of the tissues when submitted to a certain impact or acceleration/deceleration scenario. An excellent compromise between accuracy and CPU efficiency is always desirable for a FEHM, For this reason, this work focuses on the improvement of an existing head model, including the study of the behavior of the brain using distinct finite element types. The finite element type and formulation is of utmost importance for the general accuracy and efficiency of the models. Several validations were performed, comparing the simulation results against experimental data. The simulations with hexahedral elements, under specific conditions, obtained more accurate results with a lower computational cost. Using hexahedrals, a comparison was also performed using two material characterizations with more than 10 years apart, using the latest finite element head model validation experiment. Overall, the newer material model displays a less stiff response, although its implementation must always depend on the overall purpose of the model it is being applied to.

Index Shipping for Efficient Replication in LSM Key-Value Stores with Hybrid KV Placement

Key-value (KV) stores based on the LSM tree have become a foundational layer in the storage stack of datacenters and cloud services. Current approaches for achieving reliability and availability favor reducing network traffic and send to replicas only new KV pairs. As a result, they perform costly compactions to reorganize data in both the primary and backup nodes, which increases device I/O traffic and CPU overhead, and eventually hurts overall system performance. In this article, we describe Tebis , an efficient LSM-based KV store that reduces I/O amplification and CPU overhead for maintaining the replica index. We use a primary-backup replication scheme that performs compactions only on the primary nodes and sends pre-built indexes to backup nodes, avoiding all compactions in backup nodes. Our approach includes an efficient mechanism to deal with pointer translation across nodes in the pre-built region index. Our results show that Tebis reduces resource utilization on backup nodes compared to performing full compactions: throughput is increased by 1.06 to 2.90×, CPU efficiency is increased by 1.21 to 2.78×, and I/O amplification is reduced by 1.7 to 3.27×, whereas network traffic increases by up to 1.32 to 3.76x.

Optimal Configuration of Omega-Kappa FF-SAR Processing for Specular and Non-Specular Targets in Altimetric Data: The Sentinel-6 Michael Freilich Study Case

In this study, the full-focusing (FF) algorithm is reviewed with the objective of optimizing it for processing data from different types of surfaces probed in altimetry. In particular, this work aims to provide a set of optimal FF processing parameters for the Sentinel-6 Michael Freilich (S6-MF) mission. The S6-MF satellite carries an advanced radar altimeter offering a wide range of potential FF-based applications which are just beginning to be explored and require prior optimization of this processing. In S6-MF, the Synthetic Aperture Radar (SAR) altimeter acquisitions are known to be aliased in the along-track direction. Depending on the target, aliasing can be tolerated or may be a severe impairment to provide the level of performance expected from FF processing. Another key aspect to consider in this optimization study is the unprecedented resolution of the FF processing, which results in a higher posting rate than the standard SAR processing. This work investigates the relationship between posting rate and noise levels and provides recommendations for optimal algorithm configurations in various scenarios, including transponder, open ocean, and specular targets like sea-ice and inland water scenes. The Omega–Kappa (WK) algorithm, which has demonstrated superior CPU efficiency compared to the back-projection (BP) algorithm, is considered for this study. But, unlike BP, it operates in the Doppler frequency domain, necessitating further precise spectral and time domain settings. Based on the results of this work, real case studies using S6-MF acquisitions are presented. We first compare S6-MF FF radargrams with Sentinel-1 (S1) images to showcase the potential of optimally configured FF processing. For highly specular surfaces such as sea-ice, distinct techniques are employed for lead signature identification. S1 relies on image-based lineic reconstruction, while S6-MF utilizes phase coherency of focalized pulses for lead detection. The study also delves into two-dimensional wave spectra derived from the amplitude modulation of image/radargrams, with a focus on a coastal example. This case is especially intriguing, as it vividly illustrates different sea states characterized by varying spectral peak positions over time.

CPU and GPU parallel efficiency of ARM based single board computing cluster for CFD applications

This paper examines the parallel efficiency of an ARM-based single board computing cluster made of 16 Raspberry Pi 4 and 8 Nvidia Jetson Nano Single Board Computer, considering both CPU and GPU parallel implementation of CFD applications. It is found that the parallelization scales up to 16 Raspberry Pi 4 and 8 Nvidia Jetson Nano (maximum available on the current cluster). Moreover, it is shown that regarding the computation time, about 12 SBC are as fast as a classical computing workstation. Finally, it is shown that such cluster are energy efficient considering CFD applications.

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Job CPU Performance comparison based on MINIAOD reading options: Local versus remote

Hybrid Block Storage for Efficient Cloud Volume Service

The migration of traditional desktop and server applications to the cloud brings challenge of high performance, high reliability, and low cost to the underlying cloud storage. To satisfy the requirement, this article proposes a hybrid cloud-scale block storage system called Ursa . Trace analysis shows that the I/O patterns served by block storage have only limited locality to exploit. Therefore, instead of using solid state drives (SSDs) as a cache layer, Ursa proposes hybrid storage structure that directly stores primary replicas on SSDs and replicates backup replicas on hard disk drives (HDDs) . At the core of Ursa ’s hybrid storage design is an adaptive journal that can bridge the performance gap between primary SSDs and backup HDDs for random writes by transforming small backup writes into journal appends, which are then asynchronously replayed and merged to backup HDDs. To efficiently index the journal, we design a novel range-optimized merge-tree structure that combines a continuous range of keys into a single composite key {offset,length} . Ursa integrates the hybrid structure with designs for high reliability, scalability, and availability. Experiments show that Ursa in its hybrid mode achieves almost the same performance as in its SSD-only mode (storing all replicas on SSDs), and outperforms other block stores (Ceph and Sheepdog) even in their SSD-only mode while achieving much higher CPU efficiency (IOPS and throughput per core).

Towards Accelerating Data Intensive Application's Shuffle Process Using SmartNICs

The wide adoption of the emerging SmartNIC technology creates new opportunities to offload application-level computation into the networking layer, which frees the burden of host CPUs, leading to performance improvement. Shuffle, the all-to-all data exchange process, is a critical building block for network communication in distributed data-intensive applications and can potentially benefit from SmartNICs. In this paper, we develop SmartShuffle, which accelerates the data-intensive application's shuffle process by offloading various computation tasks into the SmartNIC devices. SmartShuffle supports offloading both low-level network functions, including data partitioning and network transport, and high-level computation tasks, including filtering, aggregation, and sorting. SmartShuffle adopts a coordinated offload architecture to make sender-side and receiver-side SmartNICs jointly contribute to the benefits of shuffle computation offload. SmartShuffle carefully manages the tight and time-varying computation and memory constraints on the device. We propose a liquid offloading approach, which dynamically migrates operators between the host CPU and the SmartNIC at runtime such that resources in both devices are fully utilized. We prototype SmartShuffle on the Stingray SoC SmartNICs and plug it into Spark. Our evaluation shows that SmartShuffle improves host CPU efficiency and I/O efficiency with lower job completion time. SmartShuffle outperforms Spark, and Spark RDMA by up to 40% on TPC-H.

Evaluating Task-Level CPU Efficiency for Distributed Stream Processing Systems

Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increasingly difficult. A key factor to consider when evaluating the performance of a DSPS is CPU efficiency, which is the ratio of the workload processed by the system to the CPU resources invested. In this paper, we argue that developing new performance tools for creating DSPSs that can fulfill SLOs while using minimal resources is crucial. This is especially significant in edge computing situations where resources are limited and in large cloud deployments where conserving power and reducing computing expenses are essential. To address this challenge, we present a novel task-level approach for measuring CPU efficiency in DSPSs. Our approach supports various streaming frameworks, is adaptable, and comes with minimal overheads. This enables developers to understand the efficiency of different DSPSs at a granular level and provides insights that were not previously possible.

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several state-of-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.

PetPS: Supporting Huge Embedding Models with Persistent Memory

Embedding models are effective for learning high-dimensional sparse data. Traditionally, they are deployed in DRAM parameter servers (PS) for online inference access. However, the ever-increasing model capacity makes this practice suffer from both high storage costs and long recovery time. Rapidly developing Persistent Memory (PM) offers new opportunities to PSs owing to its large capacity at low costs, as well as its persistence, while the application of PM also faces two challenges including high read latency and heavy CPU burden. To provide a low-cost but still high-performance parameter service for online inferences, we introduce PetPS, the first production-deployed PM parameter server. (1) To escape with high PM latency, PetPS introduces a PM hash index tailored for embedding model workloads, to minimize PM access. (2) To alleviate the CPU burden, PetPS offloads parameter gathering to NICs, to avoid CPU stalls when accessing parameters on PM and thus improve CPU efficiency. Our evaluation shows that PetPS can boost throughput by 1.3 -- 1.7X compared to PSs that use state-of-the-art PM hash indexes, or get 2.9 -- 5.5X latency reduction with the same throughput. Since 2020, PetPS has been deployed in Kuaishou, one world-leading short video company, and successfully reduced TCO by 30% without performance degradation.

A new continuous hybrid block method with one optimal intrastep point through interpolation and collocation

Implicit block approaches are used by a number of numerical analyzers to model mild, medium, and hard differential systems. Their excellent stability characteristics, self-starting nature, quick convergence, and large decrease in computing cost all contribute to their widespread application. With these numerical benefits in mind, a new one-step implicit block method with three intrastep grid points has been created. The major term of the local truncation error is minimized to determine which of these points is optimal. The reformulation of the suggested technique leads to a significant decrease in computing cost while maintaining the same consistency, zero-stability, mathcal{A}-stability, and convergence. Several sorts of error are calculated, together with CPU time and efficiency plot, to determine which is superior. Differential models from the fields of heat transfer, population dynamics, and chemical engineering show that the suggested method does a better job than some of the current hybrid block and implicit Radau methods with similar properties.

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection and placement is still the challenging task. This paper proposes a multi-level hierarchical fog node deployment model for the industrial environment. Moreover, the scheme utilized the IoT devices as a fog node; however, the selection depends on energy, path/location, network properties, storage, and available computing resources. Therefore, the scheme used the location-aware module before engaging the device for task computation. The framework is evaluated in terms of memory, CPU, scalability, and system efficiency; also compared with the existing approach in terms of task acceptance rate. The scheme is compared with xFogSim framework that is capable to handle workload upto 1000 devices. However, the task acceptance ratio is higher in the proposed framework due to its multi-tier model. The workload acceptance ratio is 85% reported with 3000 devices; whereas, in xFogsim the ratio is reduced to approx. 68%. The primary reason for high workload acceptation is that the proposed solution utilizes the unused resources of the user devices for computations.

A Survey on Attribute-Based Encryption Schemes Suitable for the Internet of Things

The Internet of Things (IoT) is an information service paradigm based on the integration of smart objects, mobile devices, and computers via the Internet. IoT technologies are key enablers for a multitude of applications in diverse fields, such as digital health, smart city, industrial automation, and supply chain. This raises new security and privacy challenges that can be addressed by advanced cryptographic methods. One of the most prominent is attribute-based encryption (ABE), which allows one to encrypt data while enforcing fine-grained access control on it. ABE is advantageous in many IoT applications since it allows data to be safely stored on untrusted storage, such as third-party cloud servers, hackable publish-subscribe brokers, physically accessible sensors, etc. This article surveys the ABE literature proposing schemes and solutions that are best suited for IoT applications. To do so, it first identifies three performance indicators that are key in IoT, namely, the data producer CPU efficiency, the data producer bandwidth efficiency, and the key authority bandwidth efficiency. Then, it analyzes only those schemes that are promising from the point of view of one or more indicators and, therefore, more applicable in typical IoT applications. As a further contribution, this article selects a subset of representative schemes and assesses their efficiency by thorough simulations. Such simulations show that no scheme excels in all three performance indicators at once, but some simultaneously perform well in two indicators.

Two-loop tensor integral coefficients in OpenLoops

We present a new and fully general algorithm for the automated construction of the integrands of two-loop scattering amplitudes. This is achieved through a generalisation of the open-loops method to two loops. The core of the algorithm consists of a numerical recursion, where the various building blocks of two-loop diagrams are connected to each other through process-independent operations that depend only on the Feynman rules of the model at hand. This recursion is implemented in terms of tensor coefficients that encode the polynomial dependence of loop numerators on the two independent loop momenta. The resulting coefficients are ready to be combined with corresponding tensor integrals to form scattering probability densities at two loops. To optimise CPU efficiency we have compared several algorithmic options identifying one that outperforms naive solutions by two orders of magnitude. This new algorithm is implemented in the OpenLoops framework in a fully automated way for two-loop QED and QCD corrections to any Standard Model process. The technical performance is discussed in detail for several 2 → 2 and 2 → 3 processes with up to order 105 two-loop diagrams. We find that the CPU cost scales linearly with the number of two-loop diagrams and is comparable to the cost of corresponding real-virtual ingredients in a NNLO calculation. This new algorithm constitutes a key building block for the construction of an automated generator of scattering amplitudes at two loops.

Efficient Data Mining Model for Question Retrieval and Question Analytics using Semantic Web Framework in Smart E-Learning Environment

In the field of Information recovery, the fundamental target is to discover important just as most applicable data concerning a few questions. However, the essential issue regarding recuperation has reliably been, that the request for an area is enormous so much that it has gotten very difficult to recuperate applicable information capably. In any case, with the latest progressions in profound learning and AI models, calculations, applications brilliant and computerized data recovery component matched with text examination to decide different characterizing boundaries alongside intricacy and weight-age assurance of inquiries. By focusing, the cutoff points and hardships, like CPU cost, efficiency, automation and congruity, we have assigned our information recuperation structure, particularly towards the Academic Institutional Domain to consider the interest of various association related inquiries. The aim is to make an efficient data mining and an analytical model that can automate an efficient question retrieval and analysis for complexity and weight-age determination.

CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain

Social media is a platform in which user can create, share and exchange the knowledge/information. Social media marketing is to identify the different consumer's demands and engages them to create marketing resources. The popular social media platforms are Microsoft, Snapchat, Amazon, Flipkart, Google, eBay, Instagram, Facebook, Pin interest, and Twitter. The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs. Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing. However, this disruptive technology creates some issues like fake news, insecure, inconsistency, inaccuracy and so on. These issues contribute economic instability in the society, diminishing the level of trustworthy. To overcome these issues, this paper we present blockchain as disruptive technology for social media marketing. Blockchain plays a vital role on social media marketing by providing secure to the company page in the website. The properties of disruptive potential of blockchain on social media marketing is transparency, security, reliability and immutability. This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm [BCDSMM-CNYAC]. This mining algorithm provides high CPU efficiency, high dimensionality of secure and detecting falsifying data attack in the social media marketing. For the data analysis we proposed ANOVA analysis method regarding to the factors of age, time, frequency visiting times of social media platform. For reliability analysis of data Cronbach's alpha tests are implemented.

A novel polynomial dimension decomposition method based on sparse Bayesian learning and Bayesian model averaging

The polynomial dimensional decomposition (PDD), an orthogonal polynomial-based metamodel, has received increasing attention in uncertainty quantification (UQ). Nevertheless, for complex high-dimensional problems, its computational burden may become unaffordable, which is usually called curse of dimensionality. To solve this problem, sparse regression methods can be considered to establish a sparse PDD model. However, when the design samples are limited, their computational accuracy may be low due to the enormous size of the polynomial bases. Aimed at this issue, we proposed a novel sparse PDD metamodel based on the Bayesian LASSO (least absolute shrinkage and selection operator) method and an adaptive candidate basis selection and model updating method (CBSMU). Firstly, to improve the CPU efficiency, an analytical Bayesian LASSO based on the sparse Bayesian learning is used for the regression analysis, which replaces the time-consuming Markov chain Monte Carlo sampling of the traditional method with the efficient iteration algorithm for calculating the posterior estimations. Then, to reduce the size of the polynomial bases, this study proposes the adaptive CBSMU for screening the significant candidate polynomial bases and updating the metamodel sequentially. The CBSMU can find out the candidate bases that contributes to improve the prediction accuracy in the view of Bayesian model averaging. Thus, during the process of the sparse PDD modeling, the size of candidate bases is relatively small, which facilitates to improve the final computational accuracy when the design samples are limited. We verify the proposed method using three high-dimensional numerical examples, and apply it to solve one complex high-dimensional engineering problem. The results show that the proposed method is more accurate for UQ than the two common methods with the same computational costs, and is well-suited for solving complex high-dimensional structural dynamic problem.

An Efficient OpenCL-Based FPGA Accelerator for MobileNet

Convolutional neural network is widely used in image processing and image recognition. In order to obtain higher accuracy, the computational complexity and the scale of model and parameters are increasing. FPGA has become a good choice because of its low power consumption and high flexibility. MobileNet replaces the standard convolution by depthwise convolution and pointwise convolution, which greatly reduces the computational complexity and parameters of the model in the case of less precision loss, so that it can be applied to the equipment with limited computing resources. In this paper, we propose an efficient OpenCL-based FPGA CNN accelerator to realize inferencing acceleration of MobileNet. We designed the convolution layer with modularization. We used pipeline to design a depth separable convolution parallel acceleration scheme, and made full use of the DSP resources of FPGA. This design finally achieved a good balance of hardware resources, processing speed and power consumption. The experiments show that the accelerator can reach the inferencing speed of 32.56ms, and the energy consumption is 20W. The speedup is 4x compared with CPU and energy efficiency is 3x compared with GPU.