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2a9d3bf135
...
7834c3baae
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@ -1,106 +0,0 @@
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# Interface:
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# make
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# make build
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# make format
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# make run
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# Compiler
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CC=gcc
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# CC=tcc # <= faster compilation
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# Main file
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SRC=samples.c
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OUTPUT=out/samples
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SRC_ONE_THREAD=./samples-one-thread.c
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OUTPUT_ONE_THREAD=out/samples-one-thread
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## Dependencies
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# Has no dependencies
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MATH=-lm
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## Flags
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DEBUG= #'-g'
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STANDARD=-std=c99
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WARNINGS=-Wall
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OPTIMIZED=-O3 #-O3 actually gives better performance than -Ofast, at least for this version
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OPENMP=-fopenmp
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## Formatter
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STYLE_BLUEPRINT=webkit
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FORMATTER=clang-format -i -style=$(STYLE_BLUEPRINT)
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## make build
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build: $(SRC)
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$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(OPENMP) $(MATH) -o $(OUTPUT)
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static:
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$(CC) $(OPTIMIZED) $(DEBUG) $(SRC) $(OPENMP) $(MATH) -o $(OUTPUT)
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format: $(SRC)
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$(FORMATTER) $(SRC)
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run: $(SRC) $(OUTPUT)
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OMP_NUM_THREADS=1 ./$(OUTPUT) && echo
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multi:
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OMP_NUM_THREADS=1 ./$(OUTPUT) && echo
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OMP_NUM_THREADS=2 ./$(OUTPUT) && echo
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OMP_NUM_THREADS=4 ./$(OUTPUT) && echo
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OMP_NUM_THREADS=8 ./$(OUTPUT) && echo
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OMP_NUM_THREADS=16 ./$(OUTPUT) && echo
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## Timing
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time-linux:
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@echo "Requires /bin/time, found on GNU/Linux systems" && echo
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=1 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=1 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 1 thread: |" | sed 's|$$|ms|' && echo
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=2 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=2 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 2 threads: |" | sed 's|$$|ms|' && echo
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=4 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=4 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time for 4 threads: |" | sed 's|$$|ms|' && echo
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=8 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=8 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 8 threads: |" | sed 's|$$|ms|' && echo
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=16 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=16 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 16 threads: |" | sed 's|$$|ms|' && echo
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time-linux-fastest:
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@echo "Running 100x and taking avg time: OMP_NUM_THREADS=16 $(OUTPUT)"
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@t=$$(/usr/bin/time -f "%e" -p bash -c 'for i in {1..100}; do OMP_NUM_THREADS=16 $(OUTPUT); done' 2>&1 >/dev/null | grep real | awk '{print $$2}' ); echo "scale=2; 1000 * $$t / 100" | bc | sed "s|^|Time using 16 threads: |" | sed 's|$$|ms|' && echo
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time-linux-simple:
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@echo "Requires /bin/time, found on GNU/Linux systems" && echo
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OMP_NUM_THREADS=1 /bin/time -f "Time: %es" ./$(OUTPUT) && echo
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OMP_NUM_THREADS=2 /bin/time -f "Time: %es" ./$(OUTPUT) && echo
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OMP_NUM_THREADS=4 /bin/time -f "Time: %es" ./$(OUTPUT) && echo
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OMP_NUM_THREADS=8 /bin/time -f "Time: %es" ./$(OUTPUT) && echo
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OMP_NUM_THREADS=16 /bin/time -f "Time: %es" ./$(OUTPUT) && echo
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## Profiling
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profile-linux:
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echo "Requires perf, which depends on the kernel version, and might be in linux-tools package or similar"
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echo "Must be run as sudo"
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$(CC) $(SRC) $(OPENMP) $(MATH) -o $(OUTPUT)
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# ./$(OUTPUT)
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# gprof:
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# gprof $(OUTPUT) gmon.out > analysis.txt
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# rm gmon.out
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# vim analysis.txt
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# rm analysis.txt
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# perf:
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OMP_NUM_THREADS=16 sudo perf record $(OUTPUT)
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sudo perf report
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rm perf.data
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## Install
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debian-install-dependencies:
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sudo apt-get install libomp-dev
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Binary file not shown.
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#include <math.h>
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#include <omp.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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const float PI = 3.14159265358979323846;
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#define N 1000000
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//Array helpers
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void array_print(float* array, int length)
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{
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for (int i = 0; i < length; i++) {
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printf("item[%d] = %f\n", i, array[i]);
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}
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printf("\n");
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}
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float array_sum(float* array, int length)
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{
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float output = 0.0;
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for (int i = 0; i < length; i++) {
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output += array[i];
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}
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return output;
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}
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void array_cumsum(float* array_to_sum, float* array_cumsummed, int length)
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{
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array_cumsummed[0] = array_to_sum[0];
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for (int i = 1; i < length; i++) {
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array_cumsummed[i] = array_cumsummed[i - 1] + array_to_sum[i];
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}
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}
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// Split array helpers
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int split_array_get_length(int index, int total_length, int n_threads)
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{
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return (total_length % n_threads > index ? total_length / n_threads + 1 : total_length / n_threads);
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}
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void split_array_allocate(float** meta_array, int length, int divide_into)
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{
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int split_array_length;
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for (int i = 0; i < divide_into; i++) {
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split_array_length = split_array_get_length(i, length, divide_into);
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meta_array[i] = malloc(split_array_length * sizeof(float));
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}
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}
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void split_array_free(float** meta_array, int divided_into)
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{
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for (int i = 0; i < divided_into; i++) {
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free(meta_array[i]);
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}
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free(meta_array);
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}
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float split_array_sum(float** meta_array, int length, int divided_into)
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{
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int i;
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float output = 0;
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#pragma omp parallel for reduction(+ \
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: output)
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for (int i = 0; i < divided_into; i++) {
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float own_partial_sum = 0;
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int split_array_length = split_array_get_length(i, length, divided_into);
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for (int j = 0; j < split_array_length; j++) {
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own_partial_sum += meta_array[i][j];
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}
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output += own_partial_sum;
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}
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return output;
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}
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// Pseudo Random number generator
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uint32_t xorshift32(uint32_t* seed)
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{
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// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
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// See <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>
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// https://en.wikipedia.org/wiki/Xorshift
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// Also some drama: <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>, <https://prng.di.unimi.it/>
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uint32_t x = *seed;
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x ^= x << 13;
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x ^= x >> 17;
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x ^= x << 5;
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return *seed = x;
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}
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// Distribution & sampling functions
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float rand_0_to_1(uint32_t* seed)
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{
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return ((float)xorshift32(seed)) / ((float)UINT32_MAX);
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/*
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uint32_t x = *seed;
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x ^= x << 13;
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x ^= x >> 17;
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x ^= x << 5;
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return ((float)(*seed = x))/((float) UINT32_MAX);
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*/
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// previously:
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// ((float)rand_r(seed) / (float)RAND_MAX)
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// and before that: rand, but it wasn't thread-safe.
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// See: <https://stackoverflow.com/questions/43151361/how-to-create-thread-safe-random-number-generator-in-c-using-rand-r> for why to use rand_r:
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// rand() is not thread-safe, as it relies on (shared) hidden seed.
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}
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float rand_float(float max, uint32_t* seed)
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{
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return rand_0_to_1(seed) * max;
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}
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float ur_normal(uint32_t* seed)
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{
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float u1 = rand_0_to_1(seed);
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float u2 = rand_0_to_1(seed);
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float z = sqrtf(-2.0 * log(u1)) * sin(2 * PI * u2);
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return z;
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}
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float random_uniform(float from, float to, uint32_t* seed)
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{
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return rand_0_to_1(seed) * (to - from) + from;
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}
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float random_normal(float mean, float sigma, uint32_t* seed)
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{
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return (mean + sigma * ur_normal(seed));
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}
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float random_lognormal(float logmean, float logsigma, uint32_t* seed)
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{
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return expf(random_normal(logmean, logsigma, seed));
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}
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float random_to(float low, float high, uint32_t* seed)
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{
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const float NORMAL95CONFIDENCE = 1.6448536269514722;
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float loglow = logf(low);
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float loghigh = logf(high);
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float logmean = (loglow + loghigh) / 2;
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float logsigma = (loghigh - loglow) / (2.0 * NORMAL95CONFIDENCE);
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return random_lognormal(logmean, logsigma, seed);
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}
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// Mixture function
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float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint32_t* seed)
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{
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// You can see a slightly simpler version of this function in the git history
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// or in alt/C-02-better-algorithm-one-thread/
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float sum_weights = array_sum(weights, n_dists);
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float* cumsummed_normalized_weights = malloc(n_dists * sizeof(float));
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cumsummed_normalized_weights[0] = weights[0] / sum_weights;
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for (int i = 1; i < n_dists; i++) {
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cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i] / sum_weights;
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}
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//create var holders
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float p1, result;
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int sample_index, i, own_length;
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p1 = random_uniform(0, 1, seed);
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for (int i = 0; i < n_dists; i++) {
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if (p1 < cumsummed_normalized_weights[i]) {
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result = samplers[i](seed);
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break;
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}
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}
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free(cumsummed_normalized_weights);
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return result;
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}
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// Parallization function
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void paralellize(float (*sampler)(uint32_t* seed), float** results, int n_threads){
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int sample_index, i, split_array_length;
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uint32_t** seeds = malloc(n_threads * sizeof(uint32_t*));
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for (uint32_t i = 0; i < n_threads; i++) {
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seeds[i] = malloc(sizeof(uint32_t));
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*seeds[i] = i + 1; // xorshift can't start with 0
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}
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#pragma omp parallel private(i, sample_index, split_array_length)
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{
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#pragma omp for
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for (i = 0; i < n_threads; i++) {
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split_array_length = split_array_get_length(i, N, n_threads);
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for (int j = 0; j < split_array_length; j++) {
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results[i][j] = sampler(seeds[i]);
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}
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||||||
}
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||||||
}
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||||||
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||||||
for (uint32_t i = 0; i < n_threads; i++) {
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free(seeds[i]);
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|
||||||
}
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free(seeds);
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|
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}
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|
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|
||||||
// Functions used for the BOTEC.
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||||||
// Their type has to be the same, as we will be passing them around.
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||||||
float sample_0(uint32_t* seed)
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||||||
{
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return 0;
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||||||
}
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||||||
float sample_1(uint32_t* seed)
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||||||
{
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return 1;
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}
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||||||
float sample_few(uint32_t* seed)
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||||||
{
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|
||||||
return random_to(1, 3, seed);
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||||||
}
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||||||
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||||||
float sample_many(uint32_t* seed)
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|
||||||
{
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|
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return random_to(2, 10, seed);
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|
||||||
}
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|
||||||
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||||||
float sample_mixture(uint32_t* seed){
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|
||||||
float p_a, p_b, p_c;
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|
||||||
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||||||
// Initialize variables
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||||||
p_a = 0.8;
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||||||
p_b = 0.5;
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||||||
p_c = p_a * p_b;
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||||||
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||||||
// Generate mixture
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||||||
int n_dists = 4;
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||||||
float weights[] = { 1 - p_c, p_c / 2, p_c / 4, p_c / 4 };
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|
||||||
float (*samplers[])(uint32_t*) = { sample_0, sample_1, sample_few, sample_many };
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|
||||||
|
|
||||||
return mixture(samplers, weights, n_dists, seed);
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|
||||||
}
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|
||||||
|
|
||||||
int main()
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|
||||||
{
|
|
||||||
int n_threads = omp_get_max_threads();
|
|
||||||
// printf("Max threads: %d\n", n_threads);
|
|
||||||
// omp_set_num_threads(n_threads);
|
|
||||||
float** split_array_results = malloc(n_threads * sizeof(float*));
|
|
||||||
split_array_allocate(split_array_results, N, n_threads);
|
|
||||||
|
|
||||||
paralellize(sample_mixture, split_array_results, n_threads);
|
|
||||||
printf("Sum(split_array_results, N)/N = %f\n", split_array_sum(split_array_results, N, n_threads) / N);
|
|
||||||
|
|
||||||
split_array_free(split_array_results, n_threads);
|
|
||||||
return 0;
|
|
||||||
}
|
|
83
C/samples.c
83
C/samples.c
|
@ -1,7 +1,7 @@
|
||||||
#include <math.h>
|
|
||||||
#include <omp.h>
|
#include <omp.h>
|
||||||
#include <stdint.h>
|
#include <math.h>
|
||||||
#include <stdio.h>
|
#include <stdio.h>
|
||||||
|
#include <stdint.h>
|
||||||
#include <stdlib.h>
|
#include <stdlib.h>
|
||||||
|
|
||||||
const float PI = 3.14159265358979323846;
|
const float PI = 3.14159265358979323846;
|
||||||
|
@ -64,52 +64,51 @@ float split_array_sum(float** meta_array, int length, int divided_into)
|
||||||
int i;
|
int i;
|
||||||
float output = 0;
|
float output = 0;
|
||||||
|
|
||||||
#pragma omp parallel for reduction(+ \
|
#pragma omp parallel for reduction(+:output)
|
||||||
: output)
|
for (int i = 0; i < divided_into; i++) {
|
||||||
for (int i = 0; i < divided_into; i++) {
|
float own_partial_sum = 0;
|
||||||
float own_partial_sum = 0;
|
int split_array_length = split_array_get_length(i, length, divided_into);
|
||||||
int split_array_length = split_array_get_length(i, length, divided_into);
|
for (int j = 0; j < split_array_length; j++) {
|
||||||
for (int j = 0; j < split_array_length; j++) {
|
own_partial_sum += meta_array[i][j];
|
||||||
own_partial_sum += meta_array[i][j];
|
}
|
||||||
}
|
output += own_partial_sum;
|
||||||
output += own_partial_sum;
|
}
|
||||||
}
|
return output;
|
||||||
return output;
|
|
||||||
}
|
}
|
||||||
|
|
||||||
// Pseudo Random number generator
|
// Pseudo Random number generator
|
||||||
|
|
||||||
uint32_t xorshift32(uint32_t* seed)
|
uint32_t xorshift32(uint32_t* seed)
|
||||||
{
|
{
|
||||||
// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
|
// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
|
||||||
// See <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>
|
// See <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>
|
||||||
// https://en.wikipedia.org/wiki/Xorshift
|
// https://en.wikipedia.org/wiki/Xorshift
|
||||||
// Also some drama: <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>, <https://prng.di.unimi.it/>
|
// Also some drama: <https://www.pcg-random.org/posts/on-vignas-pcg-critique.html>, <https://prng.di.unimi.it/>
|
||||||
|
|
||||||
uint32_t x = *seed;
|
uint32_t x = *seed;
|
||||||
x ^= x << 13;
|
x ^= x << 13;
|
||||||
x ^= x >> 17;
|
x ^= x >> 17;
|
||||||
x ^= x << 5;
|
x ^= x << 5;
|
||||||
return *seed = x;
|
return *seed = x;
|
||||||
}
|
}
|
||||||
|
|
||||||
// Distribution & sampling functions
|
// Distribution & sampling functions
|
||||||
|
|
||||||
float rand_0_to_1(uint32_t* seed)
|
float rand_0_to_1(uint32_t* seed){
|
||||||
{
|
return ((float) xorshift32(seed)) / ((float) UINT32_MAX);
|
||||||
return ((float)xorshift32(seed)) / ((float)UINT32_MAX);
|
/*
|
||||||
/*
|
|
||||||
uint32_t x = *seed;
|
uint32_t x = *seed;
|
||||||
x ^= x << 13;
|
x ^= x << 13;
|
||||||
x ^= x >> 17;
|
x ^= x >> 17;
|
||||||
x ^= x << 5;
|
x ^= x << 5;
|
||||||
return ((float)(*seed = x))/((float) UINT32_MAX);
|
return ((float)(*seed = x))/((float) UINT32_MAX);
|
||||||
*/
|
*/
|
||||||
// previously:
|
// previously:
|
||||||
// ((float)rand_r(seed) / (float)RAND_MAX)
|
// ((float)rand_r(seed) / (float)RAND_MAX)
|
||||||
// and before that: rand, but it wasn't thread-safe.
|
// and before that: rand, but it wasn't thread-safe.
|
||||||
// See: <https://stackoverflow.com/questions/43151361/how-to-create-thread-safe-random-number-generator-in-c-using-rand-r> for why to use rand_r:
|
// See: <https://stackoverflow.com/questions/43151361/how-to-create-thread-safe-random-number-generator-in-c-using-rand-r> for why to use rand_r:
|
||||||
// rand() is not thread-safe, as it relies on (shared) hidden seed.
|
// rand() is not thread-safe, as it relies on (shared) hidden seed.
|
||||||
}
|
}
|
||||||
|
|
||||||
float rand_float(float max, uint32_t* seed)
|
float rand_float(float max, uint32_t* seed)
|
||||||
|
@ -154,28 +153,28 @@ float random_to(float low, float high, uint32_t* seed)
|
||||||
void mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, float** results, int n_threads)
|
void mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, float** results, int n_threads)
|
||||||
{
|
{
|
||||||
// You can see a simpler version of this function in the git history
|
// You can see a simpler version of this function in the git history
|
||||||
// or in alt/C-02-better-algorithm-one-thread/
|
// or in C-02-better-algorithm-one-thread/
|
||||||
float sum_weights = array_sum(weights, n_dists);
|
float sum_weights = array_sum(weights, n_dists);
|
||||||
float* cumsummed_normalized_weights = malloc(n_dists * sizeof(float));
|
float* cumsummed_normalized_weights = malloc(n_dists * sizeof(float));
|
||||||
cumsummed_normalized_weights[0] = weights[0] / sum_weights;
|
cumsummed_normalized_weights[0] = weights[0]/sum_weights;
|
||||||
for (int i = 1; i < n_dists; i++) {
|
for (int i = 1; i < n_dists; i++) {
|
||||||
cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i] / sum_weights;
|
cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i]/sum_weights;
|
||||||
}
|
}
|
||||||
|
|
||||||
//create var holders
|
//create var holders
|
||||||
float p1;
|
float p1;
|
||||||
int sample_index, i, split_array_length;
|
int sample_index, i, split_array_length;
|
||||||
|
|
||||||
// uint32_t* seeds[n_threads];
|
// uint32_t* seeds[n_threads];
|
||||||
uint32_t** seeds = malloc(n_threads * sizeof(uint32_t*));
|
uint32_t** seeds = malloc(n_threads * sizeof(uint32_t*));
|
||||||
for (uint32_t i = 0; i < n_threads; i++) {
|
for (uint32_t i = 0; i < n_threads; i++) {
|
||||||
seeds[i] = malloc(sizeof(uint32_t));
|
seeds[i] = malloc(sizeof(uint32_t));
|
||||||
*seeds[i] = i + 1; // xorshift can't start with 0
|
*seeds[i] = i + 1; // xorshift can't start with 0
|
||||||
}
|
}
|
||||||
|
|
||||||
#pragma omp parallel private(i, p1, sample_index, split_array_length)
|
#pragma omp parallel private(i, p1, sample_index, split_array_length)
|
||||||
{
|
{
|
||||||
#pragma omp for
|
#pragma omp for
|
||||||
for (i = 0; i < n_threads; i++) {
|
for (i = 0; i < n_threads; i++) {
|
||||||
split_array_length = split_array_get_length(i, N, n_threads);
|
split_array_length = split_array_get_length(i, N, n_threads);
|
||||||
for (int j = 0; j < split_array_length; j++) {
|
for (int j = 0; j < split_array_length; j++) {
|
||||||
|
@ -191,11 +190,11 @@ void mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, float*
|
||||||
}
|
}
|
||||||
// free(normalized_weights);
|
// free(normalized_weights);
|
||||||
// free(cummulative_weights);
|
// free(cummulative_weights);
|
||||||
free(cumsummed_normalized_weights);
|
free(cumsummed_normalized_weights);
|
||||||
for (uint32_t i = 0; i < n_threads; i++) {
|
for (uint32_t i = 0; i < n_threads; i++) {
|
||||||
free(seeds[i]);
|
free(seeds[i]);
|
||||||
}
|
}
|
||||||
free(seeds);
|
free(seeds);
|
||||||
}
|
}
|
||||||
|
|
||||||
// Functions used for the BOTEC.
|
// Functions used for the BOTEC.
|
||||||
|
|
|
@ -133,7 +133,7 @@ The beautiful thing about bc is that it's an arbitrary precision calculator:
|
||||||
- it's not going to get floating point overflows, unlike practically everything else. Try `1000000001.0 ** 1000000.0` in OCaml, and you will get infinity, try p(1000000000.0, 1000000.0) and you will get a large power of 10 in bc.
|
- it's not going to get floating point overflows, unlike practically everything else. Try `1000000001.0 ** 1000000.0` in OCaml, and you will get infinity, try p(1000000000.0, 1000000.0) and you will get a large power of 10 in bc.
|
||||||
- you can always trade get more precision (at the cost of longer running times). Could be useful if you were working with tricky long tails.
|
- you can always trade get more precision (at the cost of longer running times). Could be useful if you were working with tricky long tails.
|
||||||
|
|
||||||
I decided to go with [Gavin Howard's bc](https://git.gavinhoward.com/gavin/bc), because I've been following the guy some time, and I respect him. It also had some crucial extensions, like a random number generator and allowing specifying functions and variables with names longer than one letter.
|
I decided to go with [Gavin Howard's bc](https://git.gavinhoward.com/gavin/bc), because I've been following the guy some time, and I respect him. It also had some crucial extensions, like a random number generator and
|
||||||
|
|
||||||
### Overall thoughts
|
### Overall thoughts
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue
Block a user