cache line对内存访问的影响很早就看到了，但是没有写过例子跑过，突然兴起就写了下，对这里第一个例子稍微做了改造。要注意jvm参数设置，新生代+老生代分配了2.4xG内存，新生代分了2G，eden区分了1.6g，从实际内存占用看，数组eden区使用了近1.1G的内存，剩下区域基本都是空的。另，demo是在mac上跑的。
/**
* -Xms2500m -Xmx2500m -Xcomp -Xmn2g -XX:NewRatio=10
* @author tianmai.fh
* @date 2014-03-12 16:55
*/
public class CacheLineTest {
public static final int COUNT = 3;
public static void main(String[] args) {
int[] arrs = new int[64 * 1024 * 1024 * 4]; //1g的空间，通过参数设值全放到了eden取，避免gc对测试结果的影响
equivalentWidth(arrs);
fullLoop(arrs);
}
/**
* 全循环，看跨cache line和不跨cache line的时候，花费时间对比
* @param arrs
*/
public static void fullLoop(int[] arrs){
int forLen = 256;
int forAssembly = 0;
while (forAssembly++ < COUNT) { // 循环三次，避免第一次代码优化前的影响
for (int i = 1; i <= forLen; i *= 2) {
long start = System.currentTimeMillis();
for (int j = 0, size = arrs.length; j < size; j += i) {
arrs[j] = j * 3;
}
long end = System.currentTimeMillis();
System.out.println("Full, factor: " + i + " spent " + (end - start) + " ms");
}
System.out.println();
}
}
/**
* 每次循环计算次数相同，比较跨cache line和不跨cache line的时候，花费时间的差异
* @param arrs
*/
public static void equivalentWidth(int[] arrs){
int forLen = 256;
int breakWidth = arrs.length / 256;
int forAssembly = 0;
while (forAssembly++ < COUNT) { // 循环三次，避免第一次代码优化前的影响
for (int i = 1; i <= forLen; i *= 2) {
long start = System.currentTimeMillis();
int cnt = 0;
for (int j = 0, size = arrs.length; j < size; j += i) {
arrs[j] = j;
if (++cnt > breakWidth) { //每次循环就access这么多数据
break;
}
}
long end = System.currentTimeMillis();
System.out.println("Equivalent Witdh, factor: " + i + " spent " + (end - start) + " ms");
}
System.out.println();
}
}
}
结果，等量数据访问的情况：
Equivalent Witdh, factor: 1 spent 3 ms Equivalent Witdh, factor: 2 spent 7 ms Equivalent Witdh, factor: 4 spent 2 ms Equivalent Witdh, factor: 8 spent 3 ms Equivalent Witdh, factor: 16 spent 7 ms Equivalent Witdh, factor: 32 spent 10 ms Equivalent Witdh, factor: 64 spent 10 ms Equivalent Witdh, factor: 128 spent 8 ms Equivalent Witdh, factor: 256 spent 9 ms Equivalent Witdh, factor: 1 spent 1 ms Equivalent Witdh, factor: 2 spent 1 ms Equivalent Witdh, factor: 4 spent 2 ms Equivalent Witdh, factor: 8 spent 4 ms Equivalent Witdh, factor: 16 spent 7 ms Equivalent Witdh, factor: 32 spent 10 ms Equivalent Witdh, factor: 64 spent 10 ms Equivalent Witdh, factor: 128 spent 9 ms Equivalent Witdh, factor: 256 spent 8 ms Equivalent Witdh, factor: 1 spent 2 ms Equivalent Witdh, factor: 2 spent 1 ms Equivalent Witdh, factor: 4 spent 1 ms Equivalent Witdh, factor: 8 spent 4 ms Equivalent Witdh, factor: 16 spent 7 ms Equivalent Witdh, factor: 32 spent 9 ms Equivalent Witdh, factor: 64 spent 10 ms Equivalent Witdh, factor: 128 spent 9 ms Equivalent Witdh, factor: 256 spent 8 ms
观察会发现，第二次和第三次运行，步长在1-8的时候，时间消耗是一个量级，大于等于16的时候，就是更高的量级了。
访问全部可访问数据的情况：
Full, factor: 1 spent 351 ms Full, factor: 2 spent 178 ms Full, factor: 4 spent 113 ms Full, factor: 8 spent 111 ms Full, factor: 16 spent 113 ms Full, factor: 32 spent 77 ms Full, factor: 64 spent 40 ms Full, factor: 128 spent 17 ms Full, factor: 256 spent 9 ms Full, factor: 1 spent 351 ms Full, factor: 2 spent 180 ms Full, factor: 4 spent 113 ms Full, factor: 8 spent 114 ms Full, factor: 16 spent 111 ms Full, factor: 32 spent 74 ms Full, factor: 64 spent 40 ms Full, factor: 128 spent 16 ms Full, factor: 256 spent 9 ms Full, factor: 1 spent 355 ms Full, factor: 2 spent 178 ms Full, factor: 4 spent 112 ms Full, factor: 8 spent 111 ms Full, factor: 16 spent 113 ms Full, factor: 32 spent 76 ms Full, factor: 64 spent 40 ms Full, factor: 128 spent 17 ms Full, factor: 256 spent 8 ms
这个在步长为1的时候耗时比较多，2-8的时候是一个量级的。在大于8的时候，耗时基本上是以50%的比率在递减，随着步长变长，导致的cache line重新加载次数也在递减。