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 msEquivalent 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 msEquivalent 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 msFull, 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 msFull, 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。