This commit is contained in:
Michael Zhang 2023-10-09 04:17:14 -05:00
parent 9c72bf8dbe
commit e0d966087d
6 changed files with 107 additions and 3 deletions

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@ -1,3 +1,4 @@
ARG DEBIAN_FRONTEND=noninteractive
FROM ubuntu:22.04
ENV PATH="/root/.cargo/bin:${PATH}"
@ -9,8 +10,11 @@ RUN apt update -y && apt install -y --no-install-recommends \
direnv \
git \
libomp-dev \
pandoc \
python3 \
python3-pip \
texlive-latex-base \
texlive-latex-extra \
valgrind \
;
RUN pip install poetry

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@ -4,4 +4,5 @@ lc_pthreads
*.tar.gz
dataset
out.txt
out.txt
report.pdf

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@ -1,4 +1,4 @@
.PHONY: all handin watch-openmp clean
.PHONY: all handin watch-openmp watch-report rust clean
CFLAGS := -std=c11 -fopenmp \
-I/opt/homebrew/opt/libomp/include \
@ -18,12 +18,15 @@ clean:
dataset/small \
*.o
zhan4854.tar.gz: common.c common.h lc_openmp.c lc_pthreads.c Makefile
zhan4854.tar.gz: common.c common.h lc_openmp.c lc_pthreads.c Makefile ASSIGNMENT.md report.pdf run_benchmark.sh
mkdir -p zhan4854
cp $^ zhan4854
tar -czvf $@ zhan4854
rm -r zhan4854
report.pdf: report.md
pandoc -o $@ $^
lc_openmp: lc_openmp.o common.o
$(CC) $(CFLAGS) $(LDFLAGS) -o $@ $^ -lm
@ -41,6 +44,9 @@ dataset/small/%.txt: generate_test_data.py
dataset/mnist/%.txt: generate_test_data.py
python generate_test_data.py dataset/MNIST_data.csv dataset/MNIST_label.csv dataset/mnist
watch-report:
watchexec -c clear -e md 'make report.pdf'
watch-openmp:
watchexec -c clear -e Makefile,c,h 'make lc_openmp && ./lc_openmp ./dataset/small_data.csv ./dataset/small_label.csv 10 2'

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@ -88,6 +88,11 @@ int main(int argc, char **argv) {
printf("Loss: %0.04f\n", loss);
}
double program_end_time = monotonic_seconds();
printf("Program time (compute): %0.04fs\n", total_compute_time);
printf("Program time (total): %0.04fs\n",
program_end_time - program_start_time);
free(inner_calc);
free(new_w);
free(data->buf);

70
assignments/01/report.md Normal file
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@ -0,0 +1,70 @@
---
geometry: margin=2cm
output: pdf_document
title: CSCI 5451 Assignment 1
date: \today
author: |
| Michael Zhang \<zhan4854@umn.edu\> $\cdot$ ID: 5289259
---
1. _A short description of how you went about parallelizing the classification algorithm. You should include how you decomposed the problem and why, i.e., what were the tasks being parallelized._
The parallelization I used was incredibly simple, just parallelizing outer iterations. I used this same trick for both the OpenMP and the pthreads implementations.
The reason I didn't go further was that further breaking down of the for loops incurred more overhead from managing the parallelization than was actually gained. I have run this several times and the gains were either neglient, or it actually ran slower than the serial version.
This also had to do with the fact that I had already inlined most of the calculations to require as few loops as possible, moved all allocations to the top level, and arranged my data buffer in column-major order instead since the iteration pattern was by dimension rather than by row.
2. _Timing results for 1, 2, 4, 8, and 16 threads for the classification. You should include results with outer iterations set to 10._
```
./lc_pthreads ./dataset/small_data.csv ./dataset/small_data.csv 10 1
Program time (compute): 0.0069s
./lc_pthreads ./dataset/small_data.csv ./dataset/small_data.csv 10 2
Program time (compute): 0.0027s
./lc_pthreads ./dataset/small_data.csv ./dataset/small_data.csv 10 4
Program time (compute): 0.0027s
./lc_pthreads ./dataset/small_data.csv ./dataset/small_data.csv 10 8
Program time (compute): 0.0033s
./lc_pthreads ./dataset/small_data.csv ./dataset/small_data.csv 10 16
Program time (compute): 0.0031s
./lc_pthreads ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 1
Program time (compute): 21.5287s
./lc_pthreads ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 2
Program time (compute): 10.6175s
./lc_pthreads ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 4
Program time (compute): 5.2198s
./lc_pthreads ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 8
Program time (compute): 4.5690s
./lc_pthreads ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 16
Program time (compute): 3.6433s
./lc_openmp ./dataset/small_data.csv ./dataset/small_data.csv 10 1
Program time (compute): 0.0033s
./lc_openmp ./dataset/small_data.csv ./dataset/small_data.csv 10 2
Program time (compute): 0.0017s
./lc_openmp ./dataset/small_data.csv ./dataset/small_data.csv 10 4
Program time (compute): 0.0011s
./lc_openmp ./dataset/small_data.csv ./dataset/small_data.csv 10 8
Program time (compute): 0.0020s
./lc_openmp ./dataset/small_data.csv ./dataset/small_data.csv 10 16
Program time (compute): 0.0032s
./lc_openmp ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 1
Program time (compute): 21.7196s
./lc_openmp ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 2
Program time (compute): 10.4035s
./lc_openmp ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 4
Program time (compute): 5.2449s
./lc_openmp ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 8
Program time (compute): 4.1550s
./lc_openmp ./dataset/MNIST_data.csv ./dataset/MNIST_label.csv 10 16
Program time (compute): 3.5328s
```
This data was generated using the `run_benchmark.sh > out.txt` script.
Small note: There's a part in the end of the program that performs validation on the trained model by using a train/test data set split. I didn't count this towards execution time but felt that it was important enough to keep since it ensured that my program was still behaving correctly.
```
```

18
assignments/01/run_benchmark.sh Executable file
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@ -0,0 +1,18 @@
#!/usr/bin/env bash
LC_OPENMP=${LC_OPENMP:-./lc_openmp}
LC_PTHREADS=${LC_PTHREADS:-./lc_pthreads}
SMALL_DATA=${SMALL_DATA:-./dataset/small_data.csv}
SMALL_LABEL=${SMALL_DATA:-./dataset/small_label.csv}
MNIST_DATA=${MNIST_DATA:-./dataset/MNIST_data.csv}
MNIST_LABEL=${MNIST_LABEL:-./dataset/MNIST_label.csv}
for impl in ${LC_PTHREADS} ${LC_OPENMP}; do
for dataset in "${SMALL_DATA} ${SMALL_LABEL}" "${MNIST_DATA} ${MNIST_LABEL}"; do
for t in 1 2 4 8 16; do
echo $impl $dataset 10 "$t"
$impl $dataset 10 "$t" | grep -F "Program time (compute)"
done
done
done