最大阵列大小

时间:2012-11-13 11:26:47

标签: c++ memory recursion computer-science memory-mapped-files

我有以下arduino代码执行递归合并排序。我们必须确定如何计算可在此8192B SRAM中输入的最大数组元素数量。数组元素的数量在此void setup()

中设置
int16_t Test_len = 64;

我会很乐意自己解决这个问题,但是经过几个小时后我就没那么绝望了,因为我感染了流感,所以我很想念这个讲座。

整个代码的副本。

#include <Arduino.h>
#include <mem_syms.h>

// some formatting routines to indent our messages to make it easier
// to trace the recursion.

uint8_t indent_pos = 0;
const uint8_t indent_amt = 2;

void indent_in() {
    if ( indent_pos <= 32 ) {
        indent_pos ++;
        }
    }

void indent_out() {
    if ( indent_pos >= indent_amt ) {
        indent_pos --;
        }
    }

void indent() {
    for (uint8_t i=0; i < indent_pos * indent_amt; i++) {
        Serial.print(" ");
        }
    }

// print out memory use info, s is a simple descriptive string
void mem_info(char *s) {
    indent();
    Serial.print(s);
    Serial.print(" Stack: ");
    Serial.print(STACK_SIZE);
    Serial.print(" Heap: ");
    Serial.print(HEAP_SIZE);
    Serial.print(" Avail: ");
    Serial.print(AVAIL_MEM);
    Serial.println();
    }

// call this after a malloc to confirm that the malloc worked, and 
// if not, display the message s and enter a hard loop

void assert_malloc_ok(void * mem_ptr, char *s) {
    if ( ! mem_ptr ) { 
        Serial.print("Malloc failed. ");
        Serial.print(s);
        Serial.println();
        while ( 1 ) { }
        }
    }

// call this on entry to a procedure to assue that at least required amt of
// memory is available in the free area between stack and heap if not, display
// the message s and enter a hard loop

void assert_free_mem_ok(uint16_t required_amt, char *s) {

    if ( AVAIL_MEM < required_amt ) { 
        Serial.print("Insufficient Free Memory: ");
        Serial.print(s);
        Serial.print(" require ");
        Serial.print(required_amt);
        Serial.print(", have ");
        Serial.print(AVAIL_MEM);
        Serial.println();
        while ( 1 ) { }
        }
    }

void merge(int16_t *Left, int16_t Left_len, int16_t *Right, int16_t Right_len, 
    int16_t *S) {

    // position of next element to be processed
    int Left_pos = 0;
    int Right_pos = 0;

    // position of next element of S to be specified
    // note: S_pos = Left_pos+Right_pos
    int S_pos = 0;

    // false, take from right, true take from left
    int pick_from_left = 0;

    while ( S_pos < Left_len + Right_len ) {

    // pick the smallest element at the head of the lists
    // move smallest of Left[Left_pos] and Right[Right_pos] to S[S_pos] 
    if ( Left_pos >= Left_len ) {
        pick_from_left = 0;
        }
    else if ( Right_pos >= Right_len ) {
        pick_from_left = 1;
        }
    else if ( Left[Left_pos] <= Right[Right_pos] ) {
        pick_from_left = 1;
        }
    else {
        pick_from_left = 0;
        }

    if ( pick_from_left ) {
        S[S_pos] = Left[Left_pos];
        Left_pos++;
        S_pos++;
        }
    else {
        S[S_pos] = Right[Right_pos];
        Right_pos++;
        S_pos++;
        }

    }
}


// sort in place, i.e. A will be reordered
void merge_sort(int16_t *A, int16_t A_len) {
    indent_in();
    indent();
    Serial.print("Entering merge sort: array addr ");
    Serial.print( (int) A );
    Serial.print(" len ");
    Serial.println( A_len);
    mem_info("");

    assert_free_mem_ok(128, "merge_sort");

    if ( A_len < 2 ) {
        indent_out();
        return;
        }

    if ( A_len == 2 ) {
        if ( A[0] > A[1] ) {
            int temp = A[0];
            A[0] = A[1];
            A[1] = temp;
            }
        indent_out();
        return;
        }

    // split A in half, sort left, sort right, then merge
    // left half is:  A[0], ..., A[split_point-1]
    // right half is: A[split_point], ..., A[A_len-1]

    int split_point = A_len / 2;

    indent();
    Serial.println("Doing left sort");

    merge_sort(A, split_point);

    mem_info("After left sort");

    indent();
    Serial.println("Doing right sort");

    merge_sort(A+split_point, A_len-split_point);

    mem_info("After right sort");

    // don't need the merging array S until this point
    int *S = (int *) malloc( A_len * sizeof(int) );// source of 10 bytes accumulation in heap

    assert_malloc_ok(S, "Cannot get merge buffer");

    mem_info("Doing merge");

    merge(A, split_point, A+split_point, A_len-split_point, S);

    for (int i=0; i < A_len; i++) {
        A[i] = S[i];
        }

    // now we are done with it
    free(S);

    mem_info("After free");
    indent_out();
    }

void setup() {
  Serial.begin(9600);

    // int *bad_news = (int *) malloc(4000);

    mem_info("********* THIS IS THE BEGINNING *********");
    randomSeed(analogRead(0));

    int16_t Test_len = 64;
    int16_t Test[Test_len];

    Serial.print("In: ");
    for (int16_t i=0; i < Test_len; i++) {
        Test[i] = random(0, 100);
if ( 1 ) {
        Serial.print(Test[i]);
        Serial.print(" ");
}
        }
    Serial.println();

    merge_sort(Test, Test_len);

if ( 1 ) {
    Serial.print("Out: ");
    for (int16_t i=0; i < Test_len; i++) {
        if ( i < Test_len-1 && Test[i] > Test[i+1] ) {
            Serial.print("Out of order!!");
            }

        Serial.print(Test[i]);
        Serial.print(" ");
        }
    Serial.println();
}
    }

void loop() {
    }

1 个答案:

答案 0 :(得分:0)

递归几乎是一个红色鲱鱼,最大输入数组大小等于: -

  

(total_memory - memory_allocated_for_other_stuff)/(2 * number_of_elements * sizeof array_element)

其中total_memory是系统具有的内存量,memory_allocated_for_other_stuff是程序使用的内存(如果它使用相同的内存),堆栈和其他数据,number_of_elements是数组长度,sizeof array_element是每个要排序的元素的字节数。

它是2 * number_of_elements的原因是你需要分配一个临时缓冲区来将两半合并到你的代码中SS的上限等于要排序的数组的大小,因为每个递归级别,S一半所需的大小和临时缓冲区仅在递归发生后分配。

我说递归本质几乎是一个红色的鲱鱼,因为S所需的空间每个递归步骤减半,所以如果你有记忆去做最顶层的合并,就足以做所有了递归合并也是如此。但是,每个递归步骤都会向堆栈添加固定数量(假设堆栈使用与数据相同的内存),因此memory_allocated_for_other_stuff随着递归调用的数量线性增加,即堆栈的内存为: -

  

stack_used = stack_frame_size *(log 2 (元素数量)+ 1)

其中stack_frame_size是创建堆栈帧所需的内存(堆栈上的位用于保存函数的返回地址,局部变量等)。问题是:stack_used可以超过S所需的最大空间。答案取决于堆栈框架的大小。使用电子表格,它看起来不是一个简单的问题 - 它取决于要排序的数组的大小和堆栈框架的大小,虽然看起来堆栈框架需要非常大才能导致问题。

事实证明,确定您可以排序的最大数组大小的因素之一是您要排序的数组的大小!

或者,您可以猜测一个值并查看它是否有效,使用二进制搜索缩小到特定值。

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