• (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format.
• Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between words, hide characters, use different character sets or languages other than English or any kind of manipulation).
• You are advised to make your work clear and well-presented. This includes filling your information on the cover page.
• You must use this template, failing which will result in zero mark.
• You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.
• Late submission will result in ZERO mark.
• The work should be your own, copying from students or other resources will result in ZERO mark.
• Use Times New Roman font for all your answers.

Bonus R Programming Assignment (Venn Diagrams)

This Bonus Assignment is optional. It will allow students to sharpen their R programming skills and also learn a valuable technique for visualizing data by using Venn Diagrams.

For this assignment, simply watch the following video and read the web page from the link provided.

After watching and reading the web sites, complete the examples. Feel free to use your own data if you wish to challenge yourself.

Hand in the following:

• A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted. The reason is that the assignment be graded based on your R code and not the output file. The output file will be used to verify the code commands. Also, please make sure that all comments, discussion, and conclusions regarding results are also annotated as part of your code.
• A PDF/DOC file with your output code. We are giving you more flexibility regarding how you want to present your output (tables, plots, etc.). You can either use RMD files that combine code, narrative txt, and plots or you can use word document with copy and paste from the R platform you are using. However, please remember that all output (tables, plots, comments, conclusions, etc.) shown in this file has to be generated by the same R code that you submit. This is important! Output shown that is generated using a separate code or output shown that is not supported by the submitted code will not be graded. Screenshots will not be accepted.

Essential Activities:

1. Watch all the training videos
2. Execute the example code while watching the training videos.

Notes:

1. This assignment is due Sunday at 11:59 PM EST

R Programming Assignment

For this assignment you will write an R program to complete the tasks given below. You will hand in two files for this assignment.

• A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted. The reason is that the assignment be graded based on your R code and not the output file. The output file will be used to verify the code commands. Also, please make sure that all comments, discussion, and conclusions regarding results are also annotated as part of your code.
• A PDF/DOC file with your output code. We are giving you more flexibility regarding how you want to present your output (tables, plots, etc.). You can either use RMD files that combine code, narrative txt, and plots or you can use word document with copy and paste from the R platform you are using. However, please remember that all output (tables, plots, comments, conclusions, etc.) shown in this file has to be generated by the same R code that you submit. This is important! Output shown that is generated using a separate code or output shown that is not supported by the submitted code will not be graded. Screenshots will not be accepted.

Use the following file

• R Data Set: HMEQ_Scrubbed.csv (in the zip file attached).
• The Data Dictionary in the zip file.

Note: The HMEQ_Scrubbed.csv file is a simple scrubbed file from the previous week homework. If you did more advanced scrubbing of data for last week, you may use your own data file instead. You might get better accuracy! If you decide to use your own version of HMEQ_Scrubbed.csv, please hand it in along with the other deliverables.

Step 1: Read in the Data

• Read the data into R
• List the structure of the data (str)
• Execute a summary of the data
• Print the first six records

Step 2: Classification Decision Tree

• Use the rpart library to predict the variable TARGET_BAD_FLAG
• Develop two decision trees, one using Gini and the other using Entropy
• All other parameters such as tree depth are up to you.
• Do not use TARGET_LOSS_AMT to predict TARGET_BAD_FLAG.
• Plot both decision trees
• List the important variables for both trees
• Create a ROC curve for both trees
• Write a brief summary of the decision trees discussing whether or not they make sense. Which tree would you recommend using? What type of person will default on a loan?

Step 3: Regression Decision Tree

• Use the rpart library to predict the variable TARGET_LOSS_AMT
• Develop two decision trees, one using anova and the other using poisson
• All other parameters such as tree depth are up to you.
• Do not use TARGET_BAD_FLAG to predict TARGET_LOSS_AMT.
• Plot both decision trees
• List the important variables for both trees
• Calculate the Root Mean Square Error (RMSE) for both trees
• Write a brief summary of the decision trees discussing whether or not they make sense. Which tree would you recommend using? What factors dictate a large loss of money?

Step 4: Probability / Severity Model Decision Tree (Push Yourself!)

• Use the rpart library to predict the variable TARGET_BAD_FLAG
• Use the rpart library to predict the variable TARGET_LOSS_AMT using only records where TARGET_BAD_FLAG is 1.
• Plot both decision trees
• List the important variables for both trees
• Using your models, predict the probability of default and the loss given default.
• Multiply the two values together for each record.
• Calculate the RMSE value for the Probability / Severity model.
• Comment on how this model compares to using the model from Step 3. Which one would your recommend using?

Essential Activities:

1. Watch all the training videos
2. Execute the example code while watching the training videos.

Notes:

1. This assignment is due Sunday at 11:59 PM EST

Kindly solve this file if you need any thing you can email me

Please follow the instructions exactly as mentioned in the form dont use AI

Im working on a lab where I need to design a simple CPU using a TOY ISA in Logisim. Ive completed the instruction fetch and decode stages, but Im struggling with designing the control unit to correctly generate signals for each opcode. Im also unsure how to properly connect the ALU operations with the register file and control signals. Specifically, I need help mapping opcodes to control outputs (like load, store, reg write, etc.) and verifying that the data flows correctly through the CPU.

Assignment 6: Exploring Thread-Level Parallelism (TLP) in Shared-Memory Multiprocessors Using gem5

Objective:

This assignment aims to provide students with a comprehensive understanding of Thread-Level Parallelism (TLP) and its application in shared-memory multiprocessor systems. Students will use the gem5 simulator to explore various architectures and techniques for implementing TLP, analyze the challenges and trade-offs, and examine synchronization mechanisms and memory consistency models.

Part 1: Understanding Thread-Level Parallelism

1. Introduction to TLP:

Research Literature Review Assignment:

Contemporary Research

Using available library resources, identify and read 3-5 recent peer-reviewed research papers (published within the last 5 years) that explore current challenges, novel approaches, or future directions in Thread-Level Parallelism (TLP). Focus on papers published in reputable computer architecture conferences or journals from IEEE and/or ACM.

Consider the following components for your Critical Review and Synthesis of information

Requirements: Write a comprehensive review (4-5 pages) that integrates your findings of the contemporary research.

Your review should:

• Chart the historical development of TLP, highlighting key milestones, influential ideas, and paradigm shifts in the field. Consider factors like:
• Analyze Core Concepts: Provide an in-depth analysis of fundamental TLP concepts, including:
• Critique Current Challenges: What are the major challenges facing TLP in contemporary computing systems? Consider issues like:
• How are researchers addressing these challenges? What novel techniques or approaches are being explored to overcome them? Look for examples of:
• Synthesize Future Directions: Based on your review, what are the most promising future directions for TLP research? What emerging trends or technologies could significantly impact the future of TLP? Consider areas like:

• The emergence of multi-core processors and their impact on TLP.
• Changes in programming models (e.g., from explicit threading to task-based parallelism).
• Hardware advancements that enable or constrain TLP.

• Parallelism models: What are the different ways parallelism is expressed and managed in TLP systems (e.g., shared memory, message passing)?
• Synchronization and communication: How do threads coordinate and share data effectively while minimizing overhead?
• Load balancing and scheduling: How is work distributed among threads, and how are threads scheduled to run on available cores?
• Performance metrics: How is TLP effectiveness measured? What are the trade-offs between different metrics (e.g., throughput, latency, scalability)?

• Concurrency bugs and race conditions: How are these problems detected and prevented in TLP programs?
• Scalability and Amdahl’s Law: How do we design algorithms and architectures to maximize parallelism and minimize the impact of serial portions of code?
• Heterogeneous architectures: How can TLP effectively utilize a mix of CPU cores, GPUs, and specialized accelerators?
• Energy efficiency: How can TLP be implemented in a way that balances performance with power consumption?
• New programming models or languages that make TLP easier and safer.
• Hardware enhancements to support TLP (e.g., cache coherence protocols, new synchronization primitives).
• Compiler optimizations that automatically parallelize code for TLP systems.
• Runtime systems that dynamically manage threads and resources.
• Many-core architectures with hundreds or thousands of cores.
• Integration of TLP with other forms of parallelism (e.g., SIMD, vectorization).
• The use of machine learning to guide TLP optimizations.
• Specialized hardware for specific TLP workloads.
• Look for papers that explore both theoretical and practical aspects of TLP.
• Critically evaluate the claims made in the papers. Do the authors provide sufficient evidence to support their conclusions?
• Synthesize the findings from multiple papers to identify common themes and trends.
• MinorCPU: The inorder CPU model in gem5, which simulates a simple pipelined processor.
• FloatSimdFU: The functional unit responsible for executing floating-point and SIMD instructions.
• opLat: The number of cycles it takes for a FloatSimd instruction to complete its execution within the FU.
• issueLat: The number of cycles before the next instruction can be issued to the FU.
• Daxpy Kernel: A common numerical operation that performs a scaled vector addition (y = a * x + y), often used as a benchmark in scientific computing.
• Multi-Threaded Daxpy: A version of the daxpy kernel that is parallelized across multiple threads.
• You may want to experiment with different input sizes for the daxpy kernel to see if the optimal FloatSimdFU design changes with problem size.
• Consider how the results might differ if you were using a different workload or a more complex CPU model (e.g., out-of-order execution).

Additional Tips:

Submission Requirements for Part 1:

Submit your document as a word follow in APA format following appropriate guidelines.

Part 2: Exploring Shared-Memory Architectures with gem5

Overview

In this assignment, you’ll investigate how the design of the FloatSimd functional unit (FU) in gem5’s inorder CPU model (MinorCPU) affects performance, with a particular focus on Thread-Level Parallelism (TLP). TLP is the ability to execute instructions from multiple threads concurrently, potentially improving performance on multi-core systems. You’ll explore the tradeoff between operation latency (opLat) and issue latency (issueLat) for FloatSimd instructions, analyzing how different designs impact the performance of a multi-threaded daxpy kernel.

Background

Tasks

• MinorCPU Familiarization:
• FloatSimdFU Design Space Exploration:
• Multi-Threaded Daxpy Kernel Simulation:
• Performance Analysis:
• Comparison and Evaluation:
• Report and Discussion:

• Examine the MinorCPU.py and MinorDefaultFUPool files in the gem5 source code.
• Understand the roles of opLat and issueLat within the MinorFU class.
• Note the various functional units defined in MinorDefaultFUPool.
• Modify the FloatSimdFU definition in MinorDefaultFUPool to explore different combinations of opLat and issueLat that sum to 7 cycles.
• For example:
• Create a multi-threaded implementation of the daxpy kernel where each thread handles a portion of the input vectors.
• Configure gem5 to simulate a system with multiple CPU cores.
• Utilize the annotated portion of the code provided for the daxpy kernel, adapting it for multi-threading.
• Collect detailed statistics from each simulation run, focusing on:
• Create tables or graphs to visualize the performance impact of different FloatSimdFU designs across varying thread counts (e.g., 2, 4, 8 threads).
• Prepare a concise report summarizing your findings.

• opLat = 1, issueLat = 6
• opLat = 2, issueLat = 5
• opLat = 3, issueLat = 4
• …and so on.
• Overall simulation time
• Parallel speedup (compare to single-threaded execution)
• Instructions per cycle (IPC) per thread
• Cycles per instruction (CPI) per thread
• Utilization of the FloatSimdFU
• Any other relevant metrics (e.g., thread synchronization overhead)
• How does the choice of opLat and issueLat affect thread-level parallelism?
• Is there an optimal balance between opLat and issueLat for maximizing parallel speedup?
• Do the optimal settings change with the number of threads?
• How does the FloatSimdFU design influence the ability to exploit TLP on multi-core systems?
• What are the limitations of this model for exploring TLP?
• What other factors (besides opLat and issueLat) could influence TLP in a real multi-threaded application?

Analyze the tradeoffs:

Discuss the implications of your results in the context of thread-level parallelism:

Additional Notes:

Submission:

Deliverables:

• Submit appropriate documents for parts 1 and parts 2.
• Include screenshots of your gem5 simulation outputs, configuration files, and any graphs or charts used to present data and a link to your github repository.

Evaluation Criteria:

• Research literature Review: Appropriate and detailed Memory Hierarchy Discussion
• Programming and Development Accuracy: Correct execution of the “Hello World” program in gem5.
• Screenshots: Report accurately provides screenshots depicting output and each step.
• Documentation and APA Guidelines: Clarity and completeness of the report.
• Troubleshooting: Appropriate discussion and documentation on the ability to identify and resolve issues encountered during the process.

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Important Questions & Answers

Important Questions & Answers