## Original Author/link

This activity was originally proposed by Mary Smith and Srishti Srivastava.

Paper (Srivastava2019) and slides available on-line.

## Details

See paper for details. In summary, the goal is to write the statement “More Processors Are Not Always The Best”, along with the indices of each letter.

Sequential phase. In the sequential phase, a single student is instructed to first write the statement show above (task 1) and then write the index associated with each letter (0 .. 32, 36 if including spaces) underneath (task 2). Another student times how long it takes.

In the “Uniprocessor multi-tasking phase”, the same student is asked to interleve tasks 1 and 2. In other words, they first write a letter, then write an index, then write a letter, and so on. The second student times how long it takes, and compares it to the first result.

In the “two process parallel phase”, one student completes task 1, while another student completes task 2. A third student times how long it takes, and compares the performance to tasks one and two.

In the “multi-processor parallel phase”, there are five total students. The first student is assigned to write the phrase “more processors are”, the second to writing the phrase “not always the best”, and the third student is assigned to write the indices associated student 1’s phrase. Student 4, however, must wait until student 3 completes before s/he can start writing his or her set of indices. The fifth student times how long it takes. This scenario is used to demonstrate Amdahl’s law.

All together, the activity distinguishes between parallelism and concurrency, and introduces the notion of Amdahl’s law and speedup.

## CS2013 Knowledge Unit Coverage

### PD/Parallel Fundamentals Core Tier 1

1. Distinguish using computational resources for a faster answer from managing efficient access to a shared resource. [Familiarity]

### PD/Parallel Decomposition

**Core Tier 1***:

1. Explain why synchronization is necessary in a specific parallel program. [Usage]

2. Identify opportunities to partition a serial program into independent parallel modules. [Familiarity]

**Core Tier 2**:

4. Parallelize an algorithm by applying task-based decomposition. [Usage]

5. Parallelize an algorithm by applying data-parallel decomposition. [Usage]

## PD/Parallel Algorithms, Analysis and Programming

3. Define “speed-up” and explain the notion of an algorithm’s scalability in this regard. [Familiarity]

4. Identify independent tasks in a program that may be parallelized. [Usage]

5. Characterize features of a workload that allow or prevent it from being naturally parallelized. [Familiarity]

## PD/Parallel Performance

2. Calculate the implications of Amdahl’s law for a particular parallel algorithm. [Usage]

## TCPP Topics Coverage

### Programming Topics

Know Tasks and threads: Understand what it means to create and assign work to threads/processes in a parallel program, and know of at least one way do that.

Comprehend Computation Decomposition Strategies: Understand different ways to assign computations to threads or processes

Comprehend Speedup: Understand how to compute speedup, and what it means

Know Amdahl’s Law: Know that speedup is limited by the sequential portion of a parallel program, if problem size is kept fixed

### Algorithm Topics

Comprehend Time: Recognize time as a fundamental computational resource that can be influenced by parallelism.

Comprehend Speedup: Recognize the use of parallelism either to solve a given problem instance faster or to solve larger instance in the same time (strong and weak scaling)

Comprehend/Know Scalability in algorithms and architectures: Comprehend via several examples that having access more processors does not guarantee faster execution — the notion of inherent sequentiality

Apply Dependencies: Observe how dependencies constrain the execution order of subcomputations — thereby lifting one from the limited domain of “embarrassing parallelism” to more complex computational structures

Know Dependencies: Understand the impacts of dependencies

### Cross Cutting and Advanced Topics

- Know Concurrency: The degree of inherent parallelism in an algorithm, independent of how it is executed on a machine.

## Recommended Courses

**CS0/CS1/CS2**: The authors of the activity presented the activity to students in CS0, CS1, and CS2 at several institutions.**DSA**: In addition to CS2, TCPP recommends notion of threads, computation, dependencies be covered in either DSA or Systems.**Systems**: In addition to CS2 and DSA, TCPP recommends notation of threads and dependencies be covered in Systems courses.

## Accessibility

The activity requires vision and potentially some movement. If writing is being done somewhere central, students who have mobility issues may find participating in the writing components difficult.

However, mobility-challenged students may be able to participate by being in the role of the student who times the tasks. A blind student can also fill this role by pressing a stop watch when an instructor says “go”, and stopping the timer when the instructor says “time”. The instructor can then read out-loud how much time had elapsed.

## Assessment

Paper (Srivastava2019) performs assessment on this activity and another activity (FindOldestPenny). For this particular activity, assessment was performed on 102 students (77 male, 25 female) over four institutions. The vast majority (83.3%) were aged 18-22. In addition, 29 students were Freshmen, 29 were sophomores, 26 were juniors, and 18 were seniors. Students were given the ASPECT survey that measures student engagement related to three constructs (value of activity, instructor contribution, and personal effort). Statistial significance was measured using a combination of one-way and two-way ANOVA tests. While no statistical difference was discovered for any of the three constructs over age or gender, Juniors had a statistically greater value of the activity over Freshmen, and Juniors had a statistically significant greater sense of instructor contribution than Freshmen or Sophomores. The authors suggest that that younger students may be more used to active learning strategies from K-12, while it may be more novel for older college students.

## Citations

S. Srivastava, M. Smith, A. Ghimire, and S. Gao, “Assessing the integration of parallel and distributed computing in early undergraduate computer science curriculum using unplugged activities”, in

*Proceedings of the Workshop on Education for High Performance Computing (EduHPC’19)*, 2019,*to appear*. [Online]. Available: https://tcpp.cs.gsu.edu/curriculum/sites/default/files/ws_eduhpcp110s2-file1.pdfM. Smith and S. Srivastava, “Evaluating student engagement towards integrating parallel and distributed computing (PDC) topics in undergraduate level computer science curriculum”, in

*Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19)*. 2019, pp. 1269–1269. [Online, Abstract Only]. Available: http://doi.acm.org/10.1145/3287324.3293854