DTS202TC代做、代写Java/Python程序语言

日期：2025-07-17 10:02

XJTLU Entrepreneur College (Taicang) Cover Sheet

Module code and Title DTS202TC Foundation of Parallel Computing

School Title School of AI and Advanced Computing

Assignment Title Resit Coursework

Submission Deadline 25th July, 2025, 23:59pm

Final Word Count N/A

If you agree to let the university use your work anonymously for teaching

and learning purposes, please type “yes” here.

I certify that I have read and understood the University’s Policy for dealing with Plagiarism,

Collusion and the Fabrication of Data (available on Learning Mall Online). With reference to this

policy I certify that:

• My work does not contain any instances of plagiarism and/or collusion.

My work does not contain any fabricated data.

By uploading my assignment onto Learning Mall Online, I formally declare

that all of the above information is true to the best of my knowledge and

belief.

Scoring – For Tutor Use

Student ID

Stage of

Marking

Marker

Code

Learning Outcomes Achieved （F/P/M/D）

(please modify as appropriate)

Final

Score

A B C

1

st Marker – red

pen

Moderation

– green pen

IM

Initials

The original mark has been accepted by the moderator

(please circle as appropriate):

Y / N

Data entry and score calculation have been checked by

another tutor (please circle):

Y

2

nd Marker if

needed – green

pen

For Academic Office Use Possible Academic Infringement (please tick as appropriate)

Date

Received

Days

late

Late

Penalty

☐ Category A

Total Academic Infringement Penalty

(A,B, C, D, E, Please modify where

necessary) _____________________

☐ Category B

☐ Category C

☐ Category D

☐ Category E

1

st SEMESTER 2024/2025 Assignment

Undergraduate – Year 3

DTS202TC Foundation of Parallel Computing

Submission Deadline: 25th July, 2025 23:59pm

Total Points: 100

Learning Outcomes:

A. Identify serial and parallel algorithm

B. Appreciate basic principal and techniques in devising parallel algorithm

C. Devise and implement parallel algorithms

D. Acquire basic software development skill using MPI

E. Analyze and implement common parallel algorithm patterns in a parallel programming

model such as CUDA.

F. Design experiments to analyze the performance bottlenecks in their parallel code.

G. Apply common parallel techniques to improve performance given hardware constraints.

H. Use a parallel debugger to identify and repair code defects; Use a parallel profiler to

identify performance bottlenecks in their code.

I. Apply common parallel algorithm patterns.

J. Demonstrate understanding of the major types of hardware limitations that limit parallel

program performance.

K. Identify and solve a computational problem with parallel algorithm design and program.

Avoid Plagiarism

• Do not submit work from others.

• Do not share code or work to other students.

• Do not read code or work from others, discussions between teams should be limited to

high level only.

Tasks:

Neural Networks are computationally intensive, it is crucial to speed up the training and

inference computation using parallel computing. In this assignment, you will be given a simple

two-layer neural network source code that recognise the MNIST hand written digits.

1. Firstly, build and run the given source code, identify the bottleneck of the computation

using the techniques you have leant in this subject. (20 points)

2. Provide a design of a solution to speed up using parallel programming (20 points)

3. Implement one of the techniques you have learnt in this subject to speed up the training

and inference. (hint: you can focus on speed up one function, your implementation does

not need to be perfect, focus on the correct use of libraries.) (40 points)

4. Analyze the performances, including the execution time of the serial version, parallel

with different number of processes (e.g. 2, 4, 6, 8), speedup and efficiencies. You

should also provide a line plot to represent the results. (20 points)

Submissions:

1. All source code with your parallel implementation.

2. A pdf report with explanation on the implementation and performance analysis.

The assignment must be submitted via Learning Mall to the correct drop box. Only electronic

submission is accepted and no hard copy submission.

All students must download their file and check that it is viewable after submission. Documents

may become corrupted during the uploading process (e.g. due to slow internet connections).

However, students themselves are responsible for submitting a functional and correct file for

assessments.