The Cost of Memory Fragmentation

The Backend Engineering Show with Hussein Nasser

Fragmentation is a very interesting topic to me, especially when it comes to memory. While virtually memory does solve external fragmentation (you can still allocate logically contiguous memory in non-contiguous physical memory) it does however introduce performance delays as we jump all over the physical memory to read what appears to us for example as contiguous array in virtual memory. You see, DDR RAM consists of banks, rows and columns. Each row has around 1024 columns and each column has 64 bits which makes a row around 8kib. The cost of accessing the RAM is the cost of “opening” a row and all its columns (around 50-100 ns) once the row is opened all the columns are opened and the 8 kib is cached in the row buffer in the RAM. The CPU can ask for an address and transfer 64 bytes at a time (called bursts) so if the CPU (or the MMU to be exact) asks for the next 64 bytes next to it, it comes at no cost because the entire row is cached in the RAM. However if the CPU sends a different address in a different row the old row must be closed and a new row should be opened taking an additional 50 ns hit. So spatial access of bytes ensures efficiency, So fragmentation does hurt performance if the data you are accessing are not contiguous in physical memory (of course it doesn’t matter if it is contiguous in virtual memory). This kind of remind me of the old days of HDD and how the disk needle physically travels across the disk to read one file which prompted the need of “defragmentation” , although RAM access (and SSD NAND for that matter) isn’t as bad. Moreover, virtual memory introduces internal fragmentation because of the use of fixed-size blocks (called pages and often 4kib in size), and those are mapped to frames in physical memory. So if you want to allocate a 32bit integer (4 bytes) you get a 4 kib worth of memory, leaving a whopping 4092 allocated for the process but unused, which cannot be used by the OS. These little pockets of memory can add up as many processes. Another reason developers should take care when allocating memory for efficiency.