parallel and distributed computing research paper

Distributed Systems and Parallel Computing

No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.

From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model .

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Network infrastructure

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We're always looking for more talented, passionate people.

Journal of Parallel and Distributed Computing

Subject Area and Category

• Artificial Intelligence
• Computer Networks and Communications
• Hardware and Architecture
• Theoretical Computer Science

Academic Press Inc.

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07437315, 10960848

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Journal of Parallel and Distributed Computing

Volume 12 • Issue 12

• ISSN: 0743-7315

Editor-In-Chief: V.K. Prasanna

• 5 Year impact factor: 3.4
• Impact factor: 3.4
• Journal metrics

Researchers interested in submitting a special issue proposal should adhere to the submission g… Read more

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Researchers interested in submitting a special issue proposal should adhere to the submission guidelines.

This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing .

The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.

Research Areas Include:

• Theory of parallel and distributed computing • Parallel algorithms and their implementation • Innovative computer architectures • Parallel programming • Applications, algorithms and platforms for accelerators • Cloud, edge and fog computing • Data-intensive platforms and applications • Parallel processing of graph and irregular applications • Parallel and distributed programming models • Software tools and environments for distributed systems • Algorithms and systems for Internet of Things • Performance analysis of parallel applications • Architecture for emerging technologies e.g., novel memory technologies, quantum computing • Application-specific architectures e.g., accelerator-based and reconfigurable architecture • Interconnection network, router and network interface architecture

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Fast, Accurate and Distributed Simulation of novel HPC systems incorporating ARM and RISC-V CPUs

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Information & Contributors

Bibliometrics & citations, view options, index terms.

Computing methodologies

Modeling and simulation

Simulation support systems

Simulation environments

Simulation tools

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Massively parallel and high-performance simulations

Electronic design automation

Modeling and parameter extraction

Emerging technologies

Analysis and design of emerging devices and systems

Emerging simulation

Network performance evaluation

Network simulations

Software and its engineering

Software organization and properties

Software system structures

Software architectures

Simulator / interpreter

Recommendations

Performance evaluation of various risc processor systems: a case study on arm, mips and risc-v.

RISC-V is a new instruction set architecture (ISA) that has emerged in recent years. Compared with previous computer instruction architectures, RISC-V has outstanding features such as simple instructions, modular instruction set and supporting ...

Low overhead dynamic binary translation on ARM

The ARMv8 architecture introduced AArch64, a 64-bit execution mode with a new instruction set, while retaining binary compatibility with previous versions of the ARM architecture through AArch32, a 32-bit execution mode. Most hardware implementations ...

A SMT-ARM simulator and performance evaluation

Exponential growth in the number of on-chip transistors with smaller size, make each generation of embedded microprocessors capable to supply more processing ability. In this paper a microarchitecture approach is proposed to make a simultaneous ...

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Published in.

• Patrizio Dazzi ,
• Gabriele Mencagli ,
• Program Chair:
• David Lowenthal ,
• Program Co-chair:
• Rosa M Badia
• SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

• SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

Association for Computing Machinery

New York, NY, United States

Publication History

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• hpc simulator
• distributed systems simulator
• Short-paper

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• RED-SEA EuroHPC
• Vitamin-V Horizon Europe

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CS 87: Parallel and Distributed Computing — Fall 2021

Announcements, course goals, course structure, class schedule, required readings, about the cs labs, working with partners, absence / assignment extension policy, academic accommodations, academic integrity policy, class resources, distributed systems, parallel systems, and cluster links, reading, writing and presentation advice, unix and programming resources.

Project Demo signup sheet is available. See the Part 3 assignment page.

Course Project Part3 assignments: project presentation, project report, project demo, and project code

Class announcements will be posted here and/or sent as email. You are expected to check your email frequently for class announcements. Questions and answers about assignments should be posted on EdStem page. All assignments are posted to the class Class Schedule . Here is a shortcut link to the current week’s assginments

Professor: Tia Newhall ,

Office hours: Wednesdays 2-4, and by appointment, Sci 249

Class Meetings::

Paper Discussion: Mondays, Section A: 1:15-2:45, Section B: 3:00-4:30, Sci 104

Lecture: Tuesdays, 1:15-2:30, Sci 104

Lab: Thursdays, 1:15-2:30, Sci 256

EdStem: Q&A Forum

GitHub: CS87 Swarthmore GitHub Org

Course Description

This course covers a broad range of topics related to parallel and distributed computing, including parallel and distributed architectures and systems, parallel and distributed programming paradigms, parallel algorithms, and scientific and other applications of parallel and distributed computing. In lecture/discussion sections, students examine both classic results as well as recent research in the field. The lab portion of the course includes programming projects using different programming paradigms, and students will have the opportunity to examine one course topic in depth through an open-ended project of their own choosing. Course topics may include: multi-core, SMP, MMP, client-server, clusters, clouds, grids, peer-to-peer systems, GPU computing, scheduling, scalability, resource discovery and allocation, fault tolerance, security, parallel I/0, sockets, threads, message passing, MPI, RPC, distributed shared memory, data parallel languages, MapReduce, parallel debugging, and applications of parallel and distributed computing.

Class will be run as a combination of lecture and seminar-style discussion. During the discussion based classes, students will read research papers prior to the class meeting that we will discuss in class. The first half of the course will focus on different parallel and distributed programming paradigms. During the second half, students will propose and carry out a semester-long research project related to parallel and/or distributed computing.

Prereqs : CS31 and CS35 required; prior upper-level CS course experience required. Designated: NSE, W (Writing Course), CS Group 2 Course

Analyze and critically discuss research papers both in writing and in class

Formulate and evaluate a hypothesis by proposing, implementing and testing a project

Relate one’s project to prior research via a review of related literature

Write a coherent, complete paper describing and evaluating a project

Orally present a clear and accessible summary of a research work

Understand the fundamental questions in parallel and distributed computing and analyze different solutions to these questions

Understand different parallel and distributed programming paradigms and algorithms, and gain practice in implementing and testing solutions using these.

CS87 is a seminar-style course. Its structure is designed as a bridge from lecture-based learning to inquiry-based cooperative learning that is the norm in post-Swarthmore experiences, be it graduate studies or work in industry. Although there will be some lecture, all work in this class is cooperative . This includes working in small groups to solve problems, to prepare for class discussion, to produce solutions to written and lab assigmnets, to deliver presentations, and to carry out all parts of the course project, The result of this type of course structure is that you are directly responsible for a large part of the success or failure of this class.

This is a very tentative schedule. It will be updated as we go.

• Introduction to Parallel and Distributed Computing

Weekly Reading :

• Chapt 5.9 (CPUs today)
• Chapt 15 intro (parallel systems)
• Parallel Computing Sections A-C (overview-arch)
• Intro to Distributed Computing, sections 1-2

Lab 0 : resources review for CS87

Paper 1 : parallel system

Lab 1 : pthreads scalability

• Parallel and Distributed Systems
• Chapt 14.4 Performance Meausres
• Read Reading Group and Reaction Notes Guide
• Reading Groups and Papers
• Paper 2 : End-to-End, NW protocols
• Thurs : experiment tools

Drop/add ends

• Distributed Systems
• Intro to Distributed Computing, read sections 1-2, skim 3
• Chapt 15.2-15.2.2 (distributed memory)

Paper 3 : Parallel Languages

Lab 2 : client/server

• Parallel Languages
• Section D: programming models
• skim Chapt 15.2.3 (MPI)
• skim Chapt 14.7 (openMP)

Paper 4 : Heterogeneous Computing

Thurs : part 1 demo

• Parallel Languages and Algorithms
• GPGPU Computing
• skim Section E.i-E.ix: parallel design
• Chapt 15.1 (gpus and cuda)

Paper 5 : Map Reduce

in lab : cuda examples

Lab 3 : cuda

• Parallel Languages/Algorithms
• skim Chapt 15.3 (cloud, MapReduce)
• Chapt 15.2 (MPI examples)

Paper 6 : Quick Read

XSEDE : get an XSEDE account (do Step 1)

Lab 4 : mpi

Course Project : general info

• Parallel Algorithms
• Peer-to-Peer Systems
• review Chapt 11.1 (memory heirarchy)

Paper 7 : Distributed Shared Memory

Sign-up : Lab 3 Demo

XSEDE : Do Steps 1-3 before Thurs

Course Project : proposal assignment

Lab 4.B : experiments

• Distributed Shared Memory
• review Chapt 14.7 (openMP)

Paper 8 : GFS

• Distributed File Systems
• Paper 9 : Cloud Computing
• Cloud Computing
• Green Computing
• Paper 10 : Failure
• Course Project : about project work week
• Project Work Week
• Course Project : Poject Work Week
• Course Project : Midway Report

Thanksgiving break (Nov 25)

• Paper 11 : Edge Computing

Course Project : Midway presentation

• Course Project : Project Part 3
• Edge Computing

Paper 12 : Security

Final Presentations, 2-5pm, 7-10pm, Sci Cntr 104

About Course Work/Policies

All work in CS87 will be done with a partner or in a small group. I will assign you to a reading group and I will assign partners for the lab assignments in the first half of the semester. You may pick your own project group for the course project. Project groups typically are two or three students in size. No solo course projects are allowed .

Most lab solutions you will submit electronically with git: you must do a git add, commit, and push before the due date to submit your solution on time. You may push your assignment multiple times, and a history of previous submissions will be saved. You are encouraged to push your work regularly.

Some assignments have additional submission requirements.

There is no required textbook for this courses. Instead, there will be required readings from on-line resources posted to the class schedule. In addition, we will also read and discuss one or two research papers most weeks. Research paper assignments will be posted to the class schedule and also be listed off the paper reading schedule (available week 2). The paper assigments will be updated over the course of the semester, so check this page weekly.

You will be assigned to a reading group for the semester. Your reading group will meet weekly to:

discuss the weekly assigned paper(s) before the in-class discussion.

write Reaction Notes to the assigned papers ( submit before 9am on Monday discussion day , and bring with you to class class discussion).

There may also be some on-line background readings off the Class Schedule related to the weekly assigned paper(s).

I encourage you to work in one of the CS labs when possible (vs. remotely logging in), particularly when working with a partner. The CS labs (rooms 240, 256, 238 and Clother basement) are open 24 hours a day, 7 days a week for you to use for CS course work. With the exception of times when a class, or lab, or ninja session is scheduled in one of these rooms, you may work anytime in a CS lab on CS course work. The overflow lab (238) is always available.

CS lab machines are for CS course work only. There are other computer lab/locations on campus that are for general-purpose computer use. Please review the CS Lab Rules about appropriate use of CS labs.

Info about CS lab machines

A list of CS lab machines specs is available here: CS lab machine specs

To see a listing of which machines are in which lab, look at hosts files:

To find the lab in which a machine is located run whereis :

Accessing the CS labs after hours

You can use your ID to gain access to the computer labs at nights and on the weekends. Just wave your ID over the onecard reader next to the lab doors. When the green light goes on, just push on the door handle to get in (the door knob will not turn). If you have issues with the door locks, send an email to [email protected] . If the building is locked, you can use your ID to enter the door between Martin and Cornell library. For this class, your ID will give you access to the labs in rooms SCI 238, 240, 256, and the Clothier basement.

For partnered lab assignments, you should follow these guidelines:

The expectation is that you and your partner are working together side by side in the lab for most, if not all, of the time you work on partnered lab assignments.

You and your partner should work on all aspects of the project together: initial top-down design, incremental testing and debugging, and final testing and code review.

If you are pair programming, where one of you types and one of you watches and assists, then you should swap roles periodically, taking turns doing each part.

At the end of a joint editing session, or as you change roles within a session, make sure that the "driver" does a git add , git commit and git push to push your changes to your shared repo, and your partner does a git pull to grab them so that both you and your partner have the latest version of your joint work in your local copies of your repo.

There may be short periods of time where you each go off and implement some small part independently. However, you should frequently come back together, talk through your changes, push and pull each other’s code from the git repository, and test your merged code together.

You should not delete or significantly alter code written by your partner when they are not present. If there is a problem in the code, then meet together to resolve it.

You and your partner are both equally responsible for initiating scheduling times when you can meet to work together, and for making time available in your schedule for working together.

If there are any issues with your partnership that you are unable to resolve, please come see me.

Taking time to design a plan for your solution together and to doing incremental implementation and testing together may seem like it is a waste of time, but in the long run it will save you a lot of time by making it less likely that you have design or logic errors in your solution, and by having a partner to help track down bugs and to help come up with solutions to problems.

Partnerships where partners work mostly independently rarely work out well and rarely result in complete, correct and robust solutions. Partnerships where partners work side-by-side for all or most of the time tend to work out very well.

All students start the course with 2 "late lab" day to be used on lab assignments only at your discretion, with no questions asked. You may not use late days on any other course work; all other course work must be submitted by its due date.

To use a late day on a lab assignment, you must email your professor after you have completed the lab and pushed to your repository. You do not need to inform anyone ahead of time. When you use late time, you should still expect to work on the newly-released lab during the following lab section meeting. The professor will always prioritize answering questions related to the current lab assignment.

Your late days will be counted at the granularity of full days and will be tracked on a per-student (NOT per-partnership) basis . That is, if you turn in an assignment five minutes after the deadline, it counts as using one day. For partnered labs, using a late day counts towards the late days for each partner. In the rare cases in which only one partner has unused late days, that partner’s late days may be used, barring a consistent pattern of abuse.

If you feel that you need an extension on an assignment or that you are unable to attend class for two or more meetings due to a medical condition (e.g., extended illness, concussion, hospitalization) or other emergency, you must contact the dean’s office and your instructors. Faculty will coordinate with the deans to determine and provide the appropriate accommodations. Note that for illnesses, the College’s medical excuse policy , states that you must be seen and diagnosed by the Worth Health Center if you would like them to contact your class dean with corroborating medical information.

Late days cannot be used for any written assignments or oral presentations in class. Reaction notes and all project components must be submitted on time.

If you believe you need accommodations for a disability or a chronic medical condition, please contact Student Disability Services via email at [email protected] to arrange an appointment to discuss your needs. As appropriate, the office will issue students with documented disabilities or medical conditions a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact Student Disability Services as soon as possible. For details about the accommodations process, visit the Student Disability Service Website .

You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services. To receive an accommodation for a course activity you must have an official Accommodations Letter and you need to meet with me to work out the details of your accommodation at least two weeks prior to any activity requiring accommodations .

The CS Department Academic Integrity Policy:

Academic honesty is required in all your work. Under no circumstances may you hand in work done with or by someone else under your own name. Discussing ideas and approaches to problems with others on a general level is encouraged , but you should never share your solutions with anyone else nor allow others to share solutions with you. You may not examine solutions belonging to someone else, nor may you let anyone else look at or make a copy of your solutions. This includes, but is not limited to, obtaining solutions from students who previously took the course or solutions that can be found online. You may not share information about your solution in such a manner that a student could reconstruct your solution in a meaningful way (such as by dictation, providing a detailed outline, or discussing specific aspects of the solution). You may not share your solutions even after the due date of the assignment.

In your solutions, you are permitted to include material which was distributed in class, material which is found in the course textbook, and material developed by or with an assigned partner. In these cases, you should always include detailed comments indicating on which parts of the assignment you received help and what your sources were.

When working on tests, exams, or similar assessments, you are not permitted to communicate with anyone about the exam during the entire examination period (even if you have already submitted your work). You are not permitted to use any resources to complete the exam other than those explicitly permitted by course policy. (For instance, you may not look at the course website during the exam unless explicitly permitted by the instructor when the exam is distributed.)

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook:

Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion.

This policy applies to all course work, including but not limited to code, written solutions (e.g. proofs, analyses, reports, etc.), exams, and so on. This is not meant to be an enumeration of all possible violations; students are responsible for seeking clarification if there is any doubt about the level of permissible communication.

The general ethos of this policy is that actions which shortcut the learning process are forbidden while actions which promote learning are encouraged. Studying lecture materials together, for example, provides an additional avenue for learning and is encouraged. Using a classmate’s solution, however, is prohibited because it avoids the learning process entirely. If you have any questions about what is or is not permissible, please contact your instructor.

In CS87 all of your work is collaborative, and you may share anything with your collaborators. If you use tools, solutions, ideas, or resources from other sources in your project, you must credit those sources. Reaction notes should be your interpretation of the work in the papers. Cutting and pasting content from the papers as your reactions violates the academic integrity policy.

Please contact me if you have any questions about what is permissible in this course.

Grades will be weighted as follows:

approx 35%: Class Participation, Paper discussion, Reaction Notes Each week we will discuss papers in a seminar style format. This course has some lecture content with in-class activities too, but much of the course content is generated by the class as a whole. You need to be present to contribute. Doing a close reading of the assigned papers and writing responses and questions prior to each class is essential to preparing for a lively and informed discussion. In this style of class, you are responsible for contributing to its content; what you get out of this class is dependent on what you put into paper discussions, presentations and other class participation.

approx 25%: Labs I will assign labs in the first part of the semester. You will do lab work with different assigned partners for each lab.

40%: Course Project You will design a project related to parallel and distributed computing that you will carry out over the second half of the semester. Projects must be done in pairs or small groups; no solo projects are allowed.

Reaction Notes Information

Assigned papers, reaction notes questions, reading groups

CS87 EdStem Q&A Forum

CS87 GitHub Org

Help using git for lab assignments

more verbose git setup for CS labs (written for CS31 students)

Introduction to Parallel Computing from Livermore Computing

Links to Parallel and Network Programming Resources MPI, OpenMP, posix threads, socket programming, CUDA…​

Some Cluster and Distributed Systems Papers

IEEE Distributed Systems Online

The Top 500 List

The Green 500 List

ParaScope IEEE Listing of Parallel Computing Sites

Research and Writing Guide the guide for doing CS course projects and written project reports

Tips for preparing a research presentation

How to read a CS research paper

Other reading, writing, presentation links

Swarthmore Writing Center and Writing Associates

My CS and Unix Help Pages and Links (make, tar, git, degugging tools, editors, programming guides, linux and parallel computing links, …​)

CS Department’s Help Pages

Dive into Systems Textbook (C programming, debugging, computer systems, intro to parallel programming, …​)

CS Project Etiquette tools and guidelines for using shared CS resources to run long-running, intensive applications.

Tools for running large Experiements (screen, nice, profing tools, script, …​)

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JSmol Viewer

Fuzzy modelling algorithms and parallel distributed compensation for coupled electromechanical systems.

1. Introduction

1.1. related work, 1.2. contributions, 2. simulation platform, 3. t–s fuzzy modelling, 3.1. fuzzy identification of a second-order system.

3.2. Optimization of the Fuzzy Parameter m

3.3. Closed-Loop System Poles Optimization

4. pdc fuzzy control, comparison of implementing a pdc vs. pd controller, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Reyes, C.; Ramos-Fernández, J.C.; Espinoza, E.S.; Lozano, R. Fuzzy Modelling Algorithms and Parallel Distributed Compensation for Coupled Electromechanical Systems. Algorithms 2024 , 17 , 391. https://doi.org/10.3390/a17090391

Reyes C, Ramos-Fernández JC, Espinoza ES, Lozano R. Fuzzy Modelling Algorithms and Parallel Distributed Compensation for Coupled Electromechanical Systems. Algorithms . 2024; 17(9):391. https://doi.org/10.3390/a17090391

Reyes, Christian, Julio C. Ramos-Fernández, Eduardo S. Espinoza, and Rogelio Lozano. 2024. "Fuzzy Modelling Algorithms and Parallel Distributed Compensation for Coupled Electromechanical Systems" Algorithms 17, no. 9: 391. https://doi.org/10.3390/a17090391

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Title: rapid gpu-based pangenome graph layout.

Abstract: Computational Pangenomics is an emerging field that studies genetic variation using a graph structure encompassing multiple genomes. Visualizing pangenome graphs is vital for understanding genome diversity. Yet, handling large graphs can be challenging due to the high computational demands of the graph layout process. In this work, we conduct a thorough performance characterization of a state-of-the-art pangenome graph layout algorithm, revealing significant data-level parallelism, which makes GPUs a promising option for compute acceleration. However, irregular data access and the algorithm's memory-bound nature present significant hurdles. To overcome these challenges, we develop a solution implementing three key optimizations: a cache-friendly data layout, coalesced random states, and warp merging. Additionally, we propose a quantitative metric for scalable evaluation of pangenome layout quality. Evaluated on 24 human whole-chromosome pangenomes, our GPU-based solution achieves a 57.3x speedup over the state-of-the-art multithreaded CPU baseline without layout quality loss, reducing execution time from hours to minutes.

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Top 10 Research Topics in Parallel and Distributed Computing

The specific pressure in locations of the internet with concurrent enhancement in the availability of big data with several users has to accurate the computing tasks in parallel. Parallel and distributed computing will take place in several research areas such as networks, software engineering, computer science, computer architecture, operating systems, algorithms, etc. At present, our research experts are providing complete research support and research guidance for all the research topics in parallel and distributed computing. The ideas based on an essential system of parallel and distributed computing are highlighted below shared memory models, mutual exclusion, concurrency, message passing, memory manipulation, etc.

  Parallel computing is deployed for the provision of high-speed power of processing where it is required and supercomputers are the best example for parallel computing. In this process, distributed computing is accustomed when the geographical locations are differing for the computers.

• Software-defined fog node in blockchain architecture & cloud computing
• Multi clustering approach in mobile edge computing
• Distributed computing & smart city services
• Geo distributed fog computing
• Service attacks in software-defined network with cloud computing
• Distributed trust protocol for IaaS cloud computing
• Large scale convolutional neural networks
• Parallel vertex-centric algorithms
• Partitioning algorithms in mobile environments
• Configuration tuning for hierarchical cloud schedulers
• Distributed computing with delay tolerant network

We provide the research work with the implementation of research algorithms, methodologies that shape the research projects with the proper execution and appropriate code implementation. 

Parallel Computing

            Parallel computing delivers the simultaneous process and it is used to save both money and time. In general, the memory in a parallel system might be two-dimensional such as disseminated and collective . The processors in parallel computing have to perform numerous tasks which are assigned to the processors concurrently.

Distributed Computing

           Distributed computing is entirely different from the parallel computing process because here in distributed computing a task is separated between several computers . In addition, the computers can pass the messages among them and the shared memory is not used. Several autonomous computers appear as one computer for the users. 

What are the Characteristics of Parallel and Distributed Computing?

• It is based on the process of communication and location of what the node have to access to other nodes
• It is the process of detecting failures and how the system is recovered as soon as possible
• The process of computing and processing has some accumulation in several machines
• The process similar task happing in several machines at a particular time
• The frankness of software structure and its enhancement
• Distribution of hardware and software data

Parallel Computing Versus Distributed Computing

• Parallel and distributed computing are different from each other. In distributed computing, several computers are seemed together as a single system for the users to perform a single task by messages among them. In parallel computing, a single task is split into several tasks and allocated to various system
• In distributed computing, the high scalability place is required for usage. In parallel computing, the place which has high speed is preferred for the performance
• A similar master clock is used for the synchronization in parallel computing and the synchronization algorithms are used in distributed computing
• Distributed computing is used to have their memory and processers. In parallel computing, one memory is shared for all the processors
• The limited scalability is used in parallel computing and without any limitations the systems are working in addition to networks in distributed computing
• There is no dependency in distributed computing and parallel, it is fully dependent on each other because the output of one process is the input of the next task
• A single system is involved in parallel computing as multiple hosts. In distributed computing, many systems are available in the computer system 

Distributed Parallel Computing

           The process of distributed parallel computing system is deployed for the functions of several computers in a single network with their allocated task . In general, we are using many applications based on the distributed and parallel computing system such as

• Grid Computing
• Cloud computing
• Distributed supercomputers
• Travel reservation
• Electronic banking
• Cloud storage system
• Internet, intranet & email system
• Peer to peer network

Below, our research experts have mentioned the pioneering research topics in parallel and distributed computing , it is a significant research system and it is used to locate the various geographical locations through computers . As per the data, the research fields in parallel and distributed computing are as follows

Recent Research Areas of Parallel and Distributed Computing

• Heterogeneous computing
• Biological & molecular computing
• Supercomputing
• Computational intelligence
• Quality development using HPC
• Distributed data storage & cloud architecture
• Federated ML & shared memory
• Fault tolerance software system
• MI & AL
• Distributed grid computing
• Web technologies
• Distribution & management system in multimedia
• Mobile crowdsensing
• IoT & multi-tier computing

           At present, we can see the issues from different sources in parallel and distributed computing . Thus, our research experts provide better research solutions for all such research challenges mentioned below. At this moment, let us discuss the significant research challenges in parallel and distributed computing.

Latest Research Issues of Parallel and Distributed Computing

• There are some additional functions in this process such as logging, intelligence, load balancing, monitoring, etc. All the above-mentioned functions are used for the visibility
• There is no appropriate message communication (messages are delivered wrongly to other nodes) that seems to breakdown the communication
• It is used to coordinate the sequence of changes in data and it is a complex issue in the distributed computing process and it leads the nodes to fall, stop and start
• It is the outline of multi-purpose stream processing
• It leads to let down the functions of nodes
• Functions of structural designs used in the linear process with rational quantity
•  It functions as a warehouse of data in significant corporations

Thus, by solving all such research challenges in parallel and distributed computing our technical experts have shared some significant requirements of parallel and distributed computing. And, that helps the research scholars to be familiar with the most substantial real-time requirements in current research topics in parallel and distributed computing research.

Future Research Directions of Parallel and Distributed Computing Projects

• Distributed memory parallel computing
• Distinctive purpose & hybrid structural design
• Accelerators & multicore functions
• Cloud Computing
• High performance & shared memory computing
• Developing domain applications
• Structure of supercomputing & applications

The research scholars can get the best guidance for handling parallel and distributed computing tools from our research and development experts. In this regard let us see about some of the important and best-distributed computing tools below

Development Tools for Parallel and Distributed Computing Projects

• DAGH & CUDA
• ARCH & MPICH
• PPGP & PADE
• Zabbix & Nimrod
• SPRNG & Apache Hadoop
• Paralib & simGrid
• Alchemi & distributed folding GUI

To this end, we believe that you get the top to bottom way out to select the research topics in parallel and distributed computing. The above information will make you a better research scholar to precede your research in parallel and distributed computing. Yet, if you want to become an expert, then you must need a better tutor. In addition, we have several research experts for the scholar’s research assistance . We are ready to provide help and clear up all your difficulties at any stage. So, you can enrich your skills through our keen help.

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Game Programming Meets Parallel Computing: Teaching Parallel Computing Concepts to Game Programmers

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• Neil Patrick Del Gallego 40 , 41  

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With the rapid advancement of the gaming industry and the demand to create high-performance games, game programmers must be well-versed in concurrent programming and parallel computing concepts. This paper presents a set of project-based learning activities for teaching parallel computing concepts to game programmers. We propose four project-based activities: (1) asynchronous image loader, (2) multi-threaded ray tracer, (3) interactive loading screen, and (4) interactive 3D model viewer. Our projects require computer graphics and game programming concepts, differentiating our projects from other parallel computing course assignments. Such projects may be of interest to undergraduate students who wish to pursue game programming in the future. Similarly, the projects may be taught to game programmers who have yet to learn about parallel computing. A pilot course delivery was conducted at De La Salle University, where 39 students have enrolled. Students have generally achieved high scores ( \(>80\%\) ), implying the effectiveness of the learning content in developing the projects.

The authors would like to give thanks to the DLSU students for giving valuable feedback about the course. The authors would like to acknowledge De La Salle University and DLSU Science Foundation for funding this research.

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Del Gallego, N.P. (2024). Game Programming Meets Parallel Computing: Teaching Parallel Computing Concepts to Game Programmers. In: Thiruchelvam, V., Alfred, R., Ismail, Z.I.B.A., Haviluddin, H., Baharum, A. (eds) Proceedings of the 4th International Conference on Advances in Computational Science and Engineering. ICACSE 2023. Lecture Notes in Electrical Engineering, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-97-2977-7_2

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EasyChair Smart CFP / Home New CFP My CFPs Watchlist Conference website Submission link Submission deadline September 23, 2024 6th Workshop on Education for High Performance Computing (EduHiPC 2024) 18 December 2024, Bengaluru, India In conjunction with the 31st IEEE International Conference on High-Performance Computing, Data, & Analytics ( HiPC 2024 ) Call for Paper Submission High Performance Computing (HPC) and, in general, Parallel and Distributed Computing (PDC) is ubiquitous. Every computing device, from a smartphone to a supercomputer, relies on parallel processing.  Compute clusters of multicore and manycore processors (CPUs and GPUs) are routinely used in many subdomains of computer science (CS) such as computer vision, data science, parallel machine learning and high performance computing. Therefore, it is important for every programmer, software professional and CS researchers to understand how parallelism and distributed computing affect problem solving. It is essential for educators to impart a range of PDC and HPC skills and knowledge at multiple levels within the curriculum of Computer Science (CS), Computer Engineering (CE), and related disciplines such as computational data science. Software industry and research laboratories require people with these skills, more so now. Thus, they now engage in extensive on-the-job training. Additionally, rapid changes in hardware platforms, languages, and programming environments increasingly challenge educators to decide what to teach and how to teach, in order to prepare students for careers that involve PDC and HPC. EduHiPC aims to provide a forum that brings together academia, industry, government, and non-profit organizations – especially from India, its vicinity, and Asia – for exploring and exchanging experiences and ideas about the inclusion of high-performance, parallel, and distributed computing into undergraduate and graduate curriculum of Computer Science, Computer Engineering, Computational Science, Computational Engineering, and computational courses for STEM and business and other non-STEM disciplines. The 6th EduHiPC (EduHiPC 2024) workshop invites unpublished manuscripts from individuals or teams from academia, industry, and other educational and research institutes from all over the world on topics about the teaching of PDC topics in the Computer Science and Computer Engineering curriculum as well as in domain-specific computational and data science and engineering curricula.  EduHiPC invites researchers, scholars, and practitioners to submit their work for consideration in either of the following two paper tracks or for posters or peachy assignments sessions. Additionally, we encourage manuscripts that validate their innovative approaches through the systematic collection and analysis of information to evaluate their performance and impact. The workshop is particularly dedicated to bringing together stakeholders from industry (hardware vendors, and research and development organizations), government labs, and academia in the context of HiPC 2024. The goal of the workshop is to hear the challenges faced by educators and professionals, to learn about various approaches to addressing these challenges, and to have opportunities to exchange ideas and solutions. We also encourage submissions related to the challenges in imparting education during this recent global pandemic and online evaluation mechanisms for PDC/HPC. This effort is in coordination with the Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER) . Topics of interest include, but are not limited to: Pedagogical issues in incorporating PDC and HPC in undergraduate and graduate education, especially in core courses. Novel ways of teaching PDC and HPC topics. Issues and experiences addressing remote synchronous and asynchronous teaching of PDC/HPC during the gone by pandemic situation and its relevance in current context. Data science and big data aspects of teaching HPC/PDC, including early experience with data science degree programs. Evidence-based educational practices for teaching HPC/PDC topics that provide evidence about what works best under what circumstances. Experience with incorporating PDC and HPC topics into core CS/CE courses and in domains. Experience and challenges with HPC education in developing countries, especially in India and her neighboring Asian countries. Computational Science and Engineering courses. Pedagogical tools, programming environments, infrastructures, languages, and projects for PDC and HPC. Employers’ experiences with new hires and expectation of the level of PDC and HPC proficiency among new graduates. Education resources based on high-level programming languages and environments such as Python, CUDA, OpenCL, OpenACC, SYCL, oneAPI, Hadoop, and Spark. Parallel and distributed models of programming and computation suitable for teaching, learning, and workforce development. Issues and experiences addressing the gender gap in computing and broadening participation of underrepresented groups. Challenges in remote teaching and evaluations, including those related to meaningful engagement of students and fair assessments. Experience of teaching large scale online courses in HPC and PDC across multiple geographies and student background SUBMISSION GUIDELINES Authors should submit papers in PDF format through the submission site ( https://easychair.org/conferences/?conf=eduhipc2024 ) We are accepting submissions for Track 1 Full Papers (6-8 pages) , Track 2 Short Papers (3-4 pages) , Posters (2-page abstracts) , and Peachy Parallel Assignments (2-page abstracts) .  Please see the details below for each category of submission. All entries must be submitted via the submission site ( https://easychair.org/conferences/?conf=eduhipc2024 ). Ensure that submissions adhere to the IEEE format https://www.ieee.org/conferences/publishing/templates.html), featuring single-spaced, double-column pages with proper inclusion of figures, tables, and references. Accepted regular and short papers will be published in the workshop proceedings and included in the IEEE Xplore digital library, and authors will present their work in a technical workshop session. Authors of accepted Posters and Peachy Assignments will present their work during the workshop poster sessions. Summary papers of all accepted posters and all accepted Peachy Assignments will also be published in the workshop proceedings.  Proceedings of the workshops are distributed at the conference and will be included in the IEEE Xplore Digital Library after the conference.  Summary papers will be written by the Poster and Peachy Assignment chairs and will include, as co-authors, all Poster and Peachy Assignment authors.  In addition, all individual abstracts, posters, and preprints of papers will be published on the CDER website.  Papers: Authors are asked to submit 6-8 page papers in pdf format for Track 1 and 3-4 page papers in pdf format for Track 2. Submissions will be reviewed based on the novelty of contributions, impact on the broader undergraduate curriculum, particularly on the core curriculum, relevance to the workshop’s goals, and, for experience papers, the results of their evaluation and the evaluation methodology.  Posters: High-quality poster presentations are an integral part of EduHiPC. We seek posters (2-page abstracts) describing recent or ongoing research in PDC Education.   Peachy Parallel Assignments: Course assignments are integral to student learning and also play an important role in student perceptions of the field. EduHiPC will include a session showcasing “Peachy Parallel Assignments” – high-quality assignments, previously tested in class, that are readily adoptable by other educators teaching topics in parallel and distributed computing.  Assignments may be previously published, but the author must have the right to publish a description of it and share all supporting materials. We are seeking assignments that are: Tested – All submitted assignments should have been used successfully in a class. Adoptable – Preference will be given to widely applicable and easy-to-adopt assignments.  Traits of such assignments include coverage of widely taught concepts, using common parallel languages and widely available hardware, having few prerequisites, and (with variations) being appropriate for different levels of students. Cool and inspirational – We want assignments that excite students and encourage them to spend time with the material.  Ideally, they would be things that students want to show off to their roommates. Assignments can cover any topics in Parallel and Distributed Computing. Preference will be given to assignments aimed at students in the early courses. Submissions (2-page abstracts) should describe the assignment and its contextual usage and include a link to a web page containing the complete set of files given to students (assignment description, supporting code, etc.). The document should cover the following items: What is the main idea of the assignment? What concepts are covered?  Who are its targeted students?  In what context have you used it?  What prerequisite material does it assume they have seen?  What are its strengths and weaknesses?  Are there any variations that may be of interest?  Authors of papers accepted as poster papers will be invited to revise their papers in a 2-page format. Authors of all accepted full and short papers must be present at the workshop. IMPORTANT DATES - EduHiPC2024 Workshop Submission site open:  September 1, 2024 Abstract Submission Deadline: September 16, 2024 (Encouraged) Full Paper submissions Deadline:  September 23, 2024 Author notifications Deadline:  October 20, 2024 Camera-ready Deadline:  November 6, 2024 All deadlines are at 11:59 PM AoE (UTC-12). Registration Fees for Accepted Papers At least one author of an accepted paper must register and present the paper as per HiPC2024 Registration Rates. ORGANIZATION COMMITTEE Sushil Prasad, University of Texas, San Antonio, USA Sheikh Ghafoor, Tennessee Tech University, USA Alan Sussman, National Science Foundation & University of Maryland, USA Ramachandran Vaidyanathan, Louisiana State University, USA Charles Weems, University of Massachusetts, USA Ashish Kuvelkar, C-DAC, India Sharad Sinha, IIT Goa, India Neelima Bayyapu, MIT, Manipal, India Workshop Co-Chairs Sushil K. Prasad, University of Texas San Antonio, USA, [email protected] Ashish Kuvelkar, C-DAC, India, [email protected] Program Co-Chairs Sharad Sinha, IIT Goa, India, [email protected] Neelima Bayyapu, MIT, Manipal, India, [email protected] 483 IMAGES Parallel vs. Distributed Computing: An Overview Parallel and Distributed Computing, Applications and Technologies Journal of Parallel and Distributed Computing template Parallel & Distributed Computing Report (PDF) Parallel Computing (PDF) THEORY OF DISTRIBUTED COMPUTING AND PARALLEL PROCESSING WITH ITS VIDEO PDC Lecture 3: Parallel Computation Speedup and Amdahl's Law Parallel & Distributed Databases : Overview Distributed Computing Lecture 8: Architectures P2 Parallel and Distributed Computing Lecture 1 Parallel and Distributed Computing: Test Exam Parallel and Distributed Computing Lecture 7b COMMENTS Journal of Parallel and Distributed Computing The publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems. Research Areas Include: PDF Future Directions for Parallel and Distributed Computing Parallel and Distributed Computing is at the center of this progress in that it aggregates multiple computational resources, such as CPU cores and machines, ... This section briefly summarizes top-level recommendations for research in parallel and distributed computing. Subsequent sections of the report provide more detail on specific research ... Advances in parallel and distributed computing and its applications The selected papers of this special issue cover a variety of interesting topics reflecting some recent developments in theoretical and practical research in both core and interdisciplinary areas of parallel and distributed computing, applications, and technologies. Distributed Systems and Parallel Computing From our company's beginning, Google has had to deal with both issues in our pursuit of organizing the world's information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and ... PDF Journal of Parallel and Distributed Computing Y. Song, T. Wo, R. Yang et al. Journal of Parallel and Distributed Computing 157 (2021) 168-178 Fig. 1. Illustration of the unreliable network environment consisting of remote servers and edge nodes. We devise two distinct caching solutions to balance the computa-tion complexity and caching effectiveness and satisfy the diverse Journal of Parallel and Distributed Computing This special issue invites research manuscripts extended from those presented at IEEE International Confe-rence on High Performance Computing, Data, & Analytics (HiPC2020) which was held virtually, December 16--18, 2020. The accepted papers will cover traditional areas of the high-performance computing, data science and analytics domains as well as emerging topics in these domains. Journal of Parallel and Distributed Computing The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems ... Parallel and Distributed Computing: Algorithms and Applications Feature papers represent the most advanced research with significant potential for high impact in the field. ... It is an undeniable fact that parallel and distributed computing is ubiquitous now in nearly all computational scenarios ranging from mainstream computing to high-performance and/or distributed architectures such as cloud ... Journal of Parallel and Distributed Computing The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems ... Call for papers All manuscripts submission and review will be handled by Elsevier Editorial System Submission site for Journal of Parallel and Distributed Computing. All papers should be prepared according to Guide for Authors - Journal of Parallel and Distributed Computing . Manuscripts should be no longer than 40 double-spaced pages, not including the title ... Parallel and Distributed Computing Distributed systems and calculations being carried out in parallel | Explore the latest full-text research PDFs, articles, conference papers, preprints and more on PARALLEL AND DISTRIBUTED COMPUTING. PDF Journal of Parallel and Distributed Computing X. Xu, F. Wang, H. Jiang et al. Journal of Parallel and Distributed Computing 172 (2023) 51-68 Note that this paper is based on our prior work presented at the 2019 IEEE/ACM International Symposium on Quality of Service (IWQoS'19) [34]. We briefly provide the new contents beyond the prior conference version as follows. Introduction—Parallel and Distributed Computing THE research domains of parallel and distributed computing have a significant overlap. With the advent of general-purpose multiprocessors, this overlap is bound to increase. This Special Issue attempts to draw together several papers from both of these separate research domains to illustrate commonalty and to encourage greater interaction among researchers in the two communities. Towards a Scalable and Efficient PGAS-based Distributed OpenMP MPI+X has been the de facto standard for distributed memory parallel programming. It is widely used primarily as an explicit two-sided communication model, which often leads to complex and error-prone code. Alternatively, PGAS model utilizes efficient one-sided communication and more intuitive communication primitives. In this paper, we present a novel approach that integrates PGAS concepts ... PDF Efficient Parallel Computing for Machine Learning at Scale racy is lower than 80% when we scale the batch size to 8K. After a comprehensive tuning of the learning rate and warmup, we observe that the Sync method's accuracy is slightly lower than the EA. wild asynchronous method for batch s. ze = 8K (Figure 2.4). The Sync method uses eight machines. The Async EA-wil. (PDF) Distributed Computing: An Overview The differences in calculation distribution and parallel computing, along with terminology, assignment of tasks, performance parameters, benefits and range of distributed computing, and parallel ... Quantum Algorithms and Simulation for Parallel and Distributed Quantum A viable approach for building large-scale quantum computers is to interlink small-scale quantum computers with a quantum network to create a larger distributed quantum computer. When designing quantum algorithms for such a distributed quantum computer, one can make use of the added parallelization and distribution abilities inherent in the system. An added difficulty to then overcome for ... (PDF) PARALLEL AND DISTRIBUTED COMPUTING In this paper, we present a general survey on parallel computing. The main contents include parallel computer system which is the hardware platform of parallel computing, parallel algorithm which ... Guide for authors The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems ... Fast, Accurate and Distributed Simulation of novel HPC systems short-paper. Free access. Share on. Fast, Accurate and Distributed Simulation of novel HPC systems incorporating ARM and RISC-V CPUs ... HPDC '24: Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing. June 2024. 436 pages. ISBN: 9798400704130. DOI: 10.1145/3625549. Chair: Patrizio Dazzi, Co ... PDF Journal of Parallel and Distributed Computing We present and analyze Graph_Disperse_BFS (Algorithm 4), which is a BFS-based algorithm that solves Dispersion of k ≤ n robots on an arbitrary n-node graph in O((D k) (D )) time with. + + algorithm has lower run-time than the O( O(log D logk) bits of memory at each robot using the global communication model. This. +. Research on Parallel Computing Teaching: state of the art and future This research full paper identifies how the teaching of parallel computing has been developing over the years. The learning of parallel and distributed computing is fundamental for computing professionals, due to the popularization of parallel architectures. Teaching parallel computing involves theoretical concepts and the development of practical skills. Its content is dense and comprises ... Parallel and Distributed Computing Instead, there will be required readings from on-line resources posted to the class schedule. In addition, we will also read and discuss one or two research papers most weeks. Research paper assignments will be posted to the class schedule and also be listed off the paper reading schedule (available week 2). The paper assigments will be updated ... Fuzzy Modelling Algorithms and Parallel Distributed Compensation for A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the ... Journal of Parallel and Distributed Computing Journal of Parallel and Distributed Computing. Supports open access. 10.3 CiteScore. 3.4 Impact Factor. Articles & Issues. About. Publish. Order journal. Menu. Articles & Issues. Latest issue; ... Research article Full text access TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources. Jinyu Yu ... [2409.00876] Rapid GPU-Based Pangenome Graph Layout Computer Science > Distributed, Parallel, and Cluster Computing. arXiv:2409.00876 (cs) [Submitted on 2 Sep 2024] ... Pjotr Prins, Erik Garrison, Zhiru Zhang. View a PDF of the paper titled Rapid GPU-Based Pangenome Graph Layout, by Jiajie Li and 8 other authors. View PDF HTML (experimental) Top 10 Research Topics in Parallel and Distributed Computing The specific pressure in locations of the internet with concurrent enhancement in the availability of big data with several users has to accurate the computing tasks in parallel. Parallel and distributed computing will take place in several research areas such as networks, software engineering, computer science, computer architecture, operating systems, algorithms, etc. Game Programming Meets Parallel Computing: Teaching Parallel Computing With the rapid advancement of the gaming industry and the demand to create high-performance games, game programmers must be well-versed in concurrent programming and parallel computing concepts. This paper presents a set of project-based learning activities for teaching parallel computing concepts to game programmers. CFP In conjunction with the 31st IEEE International Conference on High-Performance Computing, Data, & Analytics . Call for Paper Submission. High Performance Computing (HPC) and, in general, Parallel and Distributed Computing (PDC) is ubiquitous. Every computing device, from a smartphone to a supercomputer, relies on parallel processing. essay homework paper phd project resume review speech study thesis