HW3 Grading Policy

$$score=correctness\ast 0.4+performance\ast 0.25+report$$

Correctness (40%)

• $X$: passed tests
• $N$: total number of tests

$$correctness=\frac{X}{N}\ast 100$$

Performance (25%)

(15%) sobel GPU kernel function.

• $T$: time + panelty (from the correctness-kernel scoreboard)
• $T_i$: student
  $i$'s
  $T$
• $T_{best}$: the minimum
  $T$ of all the students

$$performance=\frac{T_{best}}{T_i}\ast 100$$

(10%) Total CPU time.

• $T$: time + panelty (from the correctness scoreboard)
• $T_i$: student
  $i$'s
  $T$
• $T_{best}$: the minimum
  $T$ of all the students

$$performance=\frac{T_{best}}{T_i}\ast 100$$

Report (35%)

1. (8%+ bonus 5%) How did you parallelize the code?
   • (2%) Which CUDA APIs did you use?
     • Metion at least 3 CUDA APIs used.
   • Which functions are ported to CUDA? How did you distribute the workload to blocks and threads?
     • (1%) Mention at least 1 function ported to CUDA.
     • (2%) Elaberate on how they distribute the workload distribution to blocks and threads
     • (bonus, 5%) Conduct experiment to show their workload distribution is better than some other combinations.
   • How do you implement shared memory?
     • (1%) Mention which variables are stored in shared memory.
     • (2%) Explain why they stored them in shared memory.
2. (5%) Which optimization techniques did you apply to your code?
   • Metion at least 1 optimization technique they used, excluding simple parallelization with CUDA and simple shared memory implementation.
3. (6%) What's the difference between cudaMalloc and cudaMallocManaged? When will you pick one over another?
   • (3%) Mention at least 1 main difference
   • (1.5%) Describe 1 scenario that should use cudaMalloc
   • (1.5%) Describe 1 scenario that should use cudaMallocManaged
4. (11%) Experiment:
   • Measure the GPU kernel time using nvprof. Show the difference with and without shared memory. In addition, measure the global memory load throughput (gld_throughput) and instruction per cycle (ipc) and explain your observation.
     • (2%) Show the GPU kernel time difference of using and not using shared memory
     • (2%) Show the global memory load throughput and instruction per cycle
     • (2%) Given at least 1 observation
     • (2%) Explain some meaningful thing about the observation (e.g., possible causes)
   • Profile your program by measuring the time spend in I/O, memory copy, CPU, and kernel.
     • (1%) Show the total time
     • (0.5%) Show the time spend in I/O
     • (0.5%) Show the time spend in memory copy
     • (0.5%) Show the time spend in CPU
     • (0.5%) Show the time spend in kernel
5. (5%) Pick any image that is not in the sample test cases, run your implementation with the image, and showcase both the input and output in your report.
   • Show both the input and output image
6. (optional, 5%) Any constructive suggestions or feedback for the homework are welcome.
   • At least 1 meaningful suggestions or constructive feedback to the assignment or spec

tags: grading policy