Analyze the code snippets below regarding the creation of a word cloud using Python (libraries: wordcloud, matplotlib.pyplot). Code Snippets: plt.imshow(word_cloud) plt.show() text = “””I like that George Mason offers an inclusive campus… [truncated text] …from in person to online.””” plt.axis(“off”) word_cloud.generate(text) word_cloud = WordCloud(random_state=433, background_color=”darkgreen”) Constraint: You must ensure the axis is turned off after the word cloud image is prepared for display, but before the final command to show the plot. Which of the following represents the correct execution sequence to generate the plot? WordCloud Output:

How did the 14th amendment change American governance?

Differentiate task-based and data-based parallelism. Give a real scientific workload example for each and explain why your chosen example fits each model.

You are given the following C++ program that performs naïve matrix multiplication for increasing matrix sizes: // Naive square matrix multiplication: C = A * B (all n x n)void matmul(const std::vector &A,            const std::vector &B,            std::vector &C) {     int n = A.size();     for (int i = 0; i < n; ++i)        for (int j = 0; j < n; ++j)            for (int k = 0; k < n; ++k)                C[i][j] += A[i][k] * B[k][j];}   Assume the main() function measures the runtime for matrix sizes n = 100, 200, 400, 800, 1600.  The computational complexity (i.e. the number of floating-point operations) performed by matmul() is proportional to n3 (written as O(n3)).  Answer the following:   (a) If the time for  n = 200  is measured to be 0.25 seconds, estimate the expected runtime for: n = 400 n = 800 Assume ideal cubic scaling (O(n3)) (b) In reality, the measured execution times for large matrices (e.g., n = 1600 ) are often much worse than the ideal cubic prediction. Explain two reasons related to memory hierarchy or cache behavior that cause this slowdown. (c) Explain why matrix multiplication is embarrassingly parallel at the level of output elements, and briefly describe how OpenMP could parallelize the outer loops. Suppose a student parallelizes the i loop with OpenMP and obtains the following runtimes: threads time (s) 1 8.0 4 2.8 8 1.9 Compute for 8 threads: speedup efficiency Then state one likely bottleneck limiting scalability.

Explain why understanding computer hardware is essential for high-performance computing. In your answer, discuss, for example: How hardware trends (e.g., GPUs, accelerators, multicore systems) influence software design How knowledge of hardware helps identify and avoid performance bottlenecks Why effective HPC developers and system designers must understand multiple layers of the computing stack (hardware, compiler, OS, and applications) How awareness of hardware limitations leads to more portable and scalable code

Stephanie is the wage earner in a “typical family” with $38,000 gross annual income. Use the easy method to determine how much insurance she should carry.

Answer the questions below.  To get credit you must answer the questions, ” I do not know” will not count. a) What is one thing you will use from this class in your life right away? b) What is one thing you think could have been improved or one thing that you liked about the class?

Using the tax table below, if a person had taxable income of $15,000 how much would they pay in the 10%Bracket[AnswerA], 12%Bracket[AnswerB], 22%Bracket[AnswerC], MarginalTaxRate[AnswerD]? 10% Up to $9,875 12% $9,876–$40,125 22% $40,126–$85,525 24% $85,526–$163,300 32% $163,301–$207,350 35% $207,351–$518,400 37% Over $518,401

If a bond is quoted in the newspaper at 81.5, what is its price? (Round your answer to 2 decimal places.)

If a $10,000 investment earns a 4.4 percent annual return, what should its value be after 1 year?