Researcher: When writing code to perform a set of computations in a single sequential process, best practices dictate the minimization...

GMAT Two Part Analysis : (TPA) Questions

Source: Mock

Two Part Analysis

Verbal - RC

HARD

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Notes

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Researcher: When writing code to perform a set of computations in a single sequential process, best practices dictate the minimization of redundant computations performed by the computer by using clever tricks to use the results of previous computations still in memory rather than starting each computation anew. In good multiprocess code, on the other hand, redundant computations are often performed in the interest of reducing the communication of results between processes. This allows the processes to complete the whole set of computations more quickly.

In summary, good multiprocess coding deemphasizes the \(\mathrm{1}\) in favor of emphasizing the \(\mathrm{2}\).

minimization of total number of computations performed

minimization of total time to complete the set of computations

maximization of total time to complete the set of computations

maximization of total number of computations performed

maximization of redundancy in computations performed

Solution

Phase 1: Owning the Dataset

Argument Analysis Table

Passage Statement	Analysis & Implications
"When writing code to perform a set of computations in a single sequential process, best practices dictate the minimization of redundant computations"	Core Fact: Single process coding prioritizes computational efficiency Visualization: If we need results \(\mathrm{A, B, and C}\), and B uses A's result, we compute A once and reuse it Logical Connection: This reduces total computations performed What We Can Conclude: Single process = minimize redundancy
"using clever tricks to use the results of previous computations still in memory"	Core Fact: Memory reuse is the method for avoiding redundancy Visualization: Store result of \(5 \times 3 = 15\), then use 15 directly instead of recalculating Logical Connection: Supports minimization of computations What We Can Conclude: Efficiency through reuse is the goal
"In good multiprocess code, on the other hand, redundant computations are often performed"	Core Fact: Multiprocess coding accepts redundancy Visualization: Process 1 calculates \(5 \times 3 = 15\), Process 2 also calculates \(5 \times 3 = 15\) independently Logical Connection: Direct contrast with single process approach What We Can Conclude: Multiprocess sacrifices computational efficiency
"in the interest of reducing the communication of results between processes"	Core Fact: Redundancy serves to minimize inter-process communication Visualization: Rather than Process 1 sending "15" to Process 2, Process 2 recalculates it Logical Connection: Trade-off between computation and communication What We Can Conclude: Communication reduction is prioritized
"This allows the processes to complete the whole set of computations more quickly"	Core Fact: The ultimate goal is faster completion time Visualization: Despite doing more total computations, parallel execution finishes sooner Logical Connection: Time efficiency trumps computational efficiency What We Can Conclude: Speed is the primary objective in multiprocess coding

Key Patterns Identified

Established Facts:
- Single process minimizes redundant computations
- Multiprocess accepts redundant computations
- Multiprocess reduces communication overhead
- Multiprocess completes faster overall
Core Trade-off: Computational efficiency vs. time efficiency
Fundamental Shift: From minimizing work to minimizing time

Phase 2: Question Analysis & Prethinking

Understanding Each Part

Part 1 Focus: What does good multiprocess coding deemphasize?
Part 2 Focus: What does good multiprocess coding emphasize?
Relationship: These represent the two sides of the trade-off described in the passage

Valid Inferences (Prethinking)

Part 1 Possibilities:
- Deemphasizes minimization of redundant computations
- Deemphasizes minimization of total computations performed
Part 2 Possibilities:
- Emphasizes speed/time efficiency
- Emphasizes minimization of completion time

Phase 3: Answer Choice Evaluation

Analyzing Each Option:

"minimization of total number of computations performed"

What it claims: Reducing the overall count of computations
Fact Support: Single process does this; multiprocess explicitly allows redundancy
Logical Validity: Directly supported as what multiprocess deemphasizes
Part Suitability: Perfect for Part 1

"minimization of total time to complete the set of computations"

What it claims: Reducing the time needed to finish all computations
Fact Support: "allows the processes to complete... more quickly"
Logical Validity: This is the stated goal of multiprocess approach
Part Suitability: Perfect for Part 2

"maximization of total time to complete the set of computations"

What it claims: Increasing completion time
Fact Support: Contradicts the passage - multiprocess completes "more quickly"
Logical Validity: Invalid inference
Part Suitability: Neither part

"maximization of total number of computations performed"

What it claims: Actively trying to increase computations
Fact Support: While computations increase, it's not the goal
Logical Validity: Misrepresents the purpose - redundancy is accepted, not maximized
Part Suitability: Neither part

"maximization of redundancy in computations performed"

What it claims: Making redundancy the primary goal
Fact Support: Redundancy is a means, not an end
Logical Validity: Over-inference - redundancy serves time minimization
Part Suitability: Neither part

Final Answer Selection

Part 1: Good multiprocess coding deemphasizes the minimization of total number of computations performed

This is what single process coding emphasizes
Multiprocess explicitly allows redundant computations

Part 2: Good multiprocess coding emphasizes the minimization of total time to complete the set of computations

This is the stated benefit: "complete... more quickly"
The entire trade-off serves this goal

Verification

Both answers create a coherent narrative: multiprocess coding sacrifices computational efficiency (Part 1) to achieve time efficiency (Part 2), perfectly capturing the trade-off described in the passage.

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