![]() ![]() SEQUENTIAL TESTING AT INTEL FULLMost systems with four DRAM channels cannot reach full bandwidth with a single thread, because a single thread can't generate enough concurrent accesses to "fill the pipeline". At least five times faster (though not as fast as theoretical 0.7 ns). random) places? But in that case each thread would still spend 70 ns to start reading and the whole process would be limited by 7 ns. It means I was wrong? And instead of one super-fast thread that reads RAM at the maximum bandwidth we have 10 parallel threads that all read chunks from different (i.e. When I scan an array, the CPU understands that I am doing a scan and just tells the RAM to read the bytes at the speed of RAM bandwidth. I then imagined the process this way: when I traverse the linked list, each jump takes 1 random RAM access, which gives 70 ns (corresponds my measurements). This is because RAM is not really 'random' access memory and needs time to be set up for a new address. One random RAM access, on the other hand, takes about 70 ns ( ). I thought like that: memory bandwidth is (at my machine) 50 GB/s, which allows reading every 32 bytes (element size) sequentially every ~0.7 ns. You might also try running the cases with the hardware prefetchers disabled, as described at. There are lots of ways for the hardware to count certain events (especially cache transactions), and many of these counters have limitations, bugs, or definitions that may not line up with what you expect. SEQUENTIAL TESTING AT INTEL SOFTWAREYou will need to be very specific about what performance counter interface software you are using so that we can figure out exactly how the hardware performance counters are programmed. Sequential accesses also enable the L2 streaming prefetcher to fetch data into the 元 and/or the L2 in advance, which reduces the average time per load further. Most recent Intel processors support 10 L1 Data Cache misses, which should give roughly a factor of 10 improvement in performance. In contrast, scanning sequentially allows the processor to issue as many reads as the out-of-order hardware will support, since their addresses are all known in advance. The next load can't be issued until the data from the current cache miss provides the address to the next one. When you are traversing a linked list (or chasing a pointer chain), you will only have one outstanding load at a time. If I understand what you are doing, the biggest difference between the two cases is concurrency. Do you know if there is a way to count 'real' 元 misses, then a full random RAM access and page table lookup are performed? It is also clear that these counters does not explain the performance implications of 元 misses, because a miss during random memory access is apparently much more expensive than 元 miss when sequentially scanning the memory. But in that case the performance difference wouldn't be 100 faster. ![]() Why 0.45 and not 0.50? And why 33% miss rate? It is as if the prefetch only loads 3 lines upon 元 miss. Which is understandable.įor the array it is 0.45 accesses, 33% of which are misses. However, cache miss measurements are not that different.įor linked list it's 1.00 access and 1.00 miss per element. The performance difference is huge - 1–2 orders of magnitude. scanning a huge linked list, that was randomized in memory beforehand. ![]() I'm measuring 元 cache misses and accesses when scanning a huge array vs. ![]()
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