Parallel and Distributed Computing: Algorithms Course
Our Parallel and Distributed Algorithms course covers models of parallel computation and PRAM, speed-up and efficiency, parallel sorting, searching and graph algorithms, message passing with MPI, shared-memory programming, distributed algorithms for leader election, consensus and mutual exclusion, and fault tolerance. Lessons are live and one-to-one with a verified subject expert, who explains each concept with worked examples, solves problems from your own module and helps you prepare for exams and coursework. Suitable for computer science students at undergraduate and master's level, following university syllabi in the UK, US, Australia and India (including AKTU, VTU and Anna University style courses).
Reviewed by P. Gaurav, B.Tech in Computer Science and EngineeringLast updated
$14 – $39
per hour
Free demo class · pay only if you continue
What you will learn
Learning outcomes
- Understand parallel and distributed computing models
- Design parallel algorithms
- Analyze parallel algorithm performance
- Apply task decomposition techniques
- Understand synchronization and communication
- Design parallel sorting and searching algorithms
- Apply parallel graph algorithms
- Understand distributed algorithms and coordination
- Explain consensus and fault tolerance
- Analyze scalability and load balancing
- Develop efficient algorithms for distributed systems
Prerequisites
- Data structures and algorithms
- Design and analysis of algorithms
- Operating systems basics
- Computer networks basics
- Discrete mathematics
- Basic programming
Tags
Course structure
Syllabus(5 modules)
- Data parallelism
- Control parallelism
- PRAM model
- EREW
- CREW and CRCW
- PRAM algorithms
What is Parallel and Distributed Algorithms?
In short: this course gives one-to-one online tutoring in parallel and distributed algorithms. A verified tutor explains PRAM models, speedup and efficiency, parallel sorting, consensus, leader election and fault tolerance, with worked problems.
Parallel and distributed algorithms are algorithms designed to solve problems faster or at larger scale by using many processors at once, either sharing memory (parallel) or communicating over a network (distributed). The subject covers models such as the PRAM, interconnection networks like meshes and hypercubes, parallel matrix and sorting algorithms, and distributed problems such as leader election and spanning trees.
This online course teaches Parallel and Distributed Algorithms one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Distributed computing help.
What does the Parallel and Distributed Algorithms course cover?
| Topic | Key content |
|---|---|
| Parallelism and PRAM | Data and control parallelism, PRAM model and variants (EREW, CREW, CRCW), PRAM algorithms, speed-up and efficiency |
| Processor organisations | Mesh, hypercube and other networks, embedding problem graphs into processor graphs |
| Parallel algorithms | Matrix multiplication, merging and sorting on different processor organisations |
| Distributed systems | Synchronous and asynchronous network models, leader election in rings |
| Distributed graph algorithms | BFS, DFS, shortest paths and spanning trees in distributed systems, broadcast and convergecast |
How do you measure a parallel algorithm's performance?
| Measure | Formula | Meaning |
|---|---|---|
| Speed-up | S(p) = T(1) ÷ T(p) | How much faster with p processors |
| Efficiency | E = S(p) ÷ p | How well processors are used |
| Cost | p × T(p) | Total work done |
| Cost-optimal | Cost = O(best sequential time) | No wasted work overall |
Amdahl's law limits speed-up: if a fraction f of the work is sequential, speed-up can never exceed 1 ÷ f.
How does leader election work in a ring?
In the LCR algorithm for a unidirectional ring with unique IDs:
- Each process sends its ID to its neighbour.
- On receiving an ID larger than its own, a process forwards it.
- Smaller IDs are discarded.
- A process that receives its own ID knows it has the largest and becomes leader.
- It announces itself; message complexity is O(n²) in the worst case, improved to O(n log n) by HS-style algorithms.
What does a worked Amdahl's law question look like?
Here is a typical question: 90% of a program can be parallelised. What speedup do 8 processors give?
| Step | Working |
|---|---|
| Amdahl's law | S = 1 ÷ ((1 − p) + p ÷ n) |
| Substitute | 1 ÷ (0.1 + 0.9 ÷ 8) |
| Calculate | 1 ÷ 0.2125 |
| Speedup | ≈ 4.7 |
Where do students lose marks in Parallel and Distributed Algorithms?
Students most often lose marks in Parallel and Distributed Algorithms through the habits below. Each is easy to fix with practice and feedback on your own work.
| Mark-loser | What to do instead |
|---|---|
| Ignoring the serial fraction | Apply Amdahl's law before claiming speedup |
| Race conditions overlooked | Identify shared data and synchronise it |
| Steps skipped | Show each step of the method |
| Definitions vague | Use precise technical definitions |
| No worked check | Verify the result with a small example |
| Diagrams missing | Draw the structure or flow before solving |
How is it assessed, and how can a tutor help?
These modules are usually assessed by written exams with algorithm analysis and complexity questions, plus assignments using MPI, OpenMP or simulation.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
How the lessons work
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Design and Analysis of Algorithms, Computer Networks and Operating Systems.
Parallel and distributed computing syllabus: who this course is for, prerequisites and study plan
A parallel and distributed computing syllabus usually covers parallel models such as PRAM, performance laws, interconnection networks, parallel algorithms, and distributed coordination such as leader election and consensus, and this course teaches these topics with a 1-to-1 tutor.
This course suits third- and fourth-year computer science students and postgraduates taking a parallel computing or distributed algorithms module.
Prerequisites: data structures and algorithms, operating systems and computer networks basics, discrete mathematics and programming.
Course length: 36 one-hour lessons across 5 modules.
| Stage | Module | What you can do afterwards |
|---|---|---|
| 1 | Parallelism and the PRAM model | Compare EREW, CREW and CRCW and apply Amdahl's law |
| 2 | Processor organisations | Compare mesh and hypercube networks |
| 3 | Parallel matrix and sorting algorithms | Analyse parallel sorting and matrix algorithms |
| 4 | Distributed systems, leader election, distributed graph algorithms | Trace ring leader election and distributed BFS |
Where it leads: this course builds on our Design and Analysis of Algorithms course and Operating Systems.
Parallel and Distributed Computing: Algorithms Course: frequently asked questions
What is the difference between parallel and distributed algorithms?
Parallel algorithms usually share memory and a clock; distributed algorithms run on separate machines that communicate by messages.
What is the PRAM model?
An idealised shared-memory parallel machine used to design and analyse parallel algorithms.
What do EREW, CREW and CRCW mean?
They describe whether reads and writes to the same memory cell can be exclusive or concurrent.
What is a hypercube network?
A network of 2ⁿ processors, each connected to n neighbours whose addresses differ by one bit.
What is Amdahl's law?
A limit on speed-up set by the sequential fraction of a program.
What is leader election?
Choosing a single coordinator among distributed processes.
Can you help with MPI or OpenMP assignments?
Tutors explain concepts and review your code; the work you submit must be your own.
Can you help me prepare for my exam?
Yes. Tutors practise algorithm analysis and model questions.
How much does a Parallel and Distributed Algorithms tutor cost?
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
Can a tutor do my assignment for me?
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
What is efficiency?
Speedup divided by the number of processors.
What is consensus?
Agreement among distributed processes on a value despite failures.
What is Gustafson's law?
Speedup can grow with problem size when the parallel part scales.
What is a Lamport clock?
A logical clock that orders events in a distributed system.
Can you help with MPI or OpenMP code?
Yes. Tutors explain parallel programming and review your own code.
What is concurrency in parallel and distributed computing?
Concurrency means several tasks are in progress during the same period and may share resources. In parallel computing, tasks run at the same time on multiple processors. In distributed computing, they run on separate machines that communicate by messages. Exams often test race conditions, synchronisation, deadlock and consistency, so your tutor practises those.
Do you offer lessons in my time zone?
Yes. Lessons are online, so you can book times that suit your time zone.
What are examples of parallel and distributed computing?
Examples of parallel computing include a GPU rendering graphics, a multi-core processor running a simulation, and training machine learning models on many processors. Examples of distributed computing include web search, cloud storage, online banking and blockchains, where separate machines cooperate over a network. Many large systems combine both approaches.
Is parallel and distributed computing hard?
It is usually considered challenging because you must reason about many things happening at once, including race conditions, synchronisation, deadlock and partial failure. Bugs can be hard to reproduce. It becomes manageable with small examples and clear models of execution, and your tutor uses worked traces and short MPI or OpenMP programs.
Can my tutor set practice between lessons?
Yes. Tutors can set questions and go through them in the next session.
What topics are in the parallel and distributed computing syllabus?
Most parallel and distributed computing syllabi include parallel architectures, the PRAM model, speedup and Amdahl's law, interconnection networks, parallel sorting and matrix algorithms, message passing with MPI, shared memory with OpenMP, and distributed topics such as clocks, leader election and consensus. Your tutor follows your own university syllabus.
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