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Scaling the Network Bandwidth

Although figure 3 suggests that the performance of REMOTE_MEMORY is significantly better than the performance of DISK, the completion time of an application even under REMOTE_MEMORY may be unacceptably high. Hopefully, the performance of REMOTE_MEMORY will be improved as soon as the Ethernet interconnection network is substituted with a faster one (e.g. FDDI, ATM, FCS, etc.). To evaluate the performance of the applications on top of faster networks, or faster disks we make detailed performance measurements that separate the completion time of the application into three factors: (i) bandwidth-dependent blocking time, (ii) useful user time, and (iii) protocol-dependent systems overhead. Using the provided time command we measure the elapsed time for each application which is the sum of factors (i)-(iii). The same command also provides the user-time (factor (ii)), and the system time (factor (iii)). If from the elapsed time, we subtract the user plus the system time, we get the time the system was idle waiting for pages to go through the interconnection network (factor (i)). By dividing this idle time with the number of page ins plus page outs, we get the average time the application waits for each page to go through the interconnection network. Assuming that an X times faster interconnection network will reduce this waiting time by a factor of X, we can predict the completion time of the application on the faster network gif by adding the measures user and system times, with the predicted blocking time.

We made all these measurements on our FFT application, and predict its performance on a system with an interconnection network which is two and ten times as fast as the Ethernet. We also predict its completion time on a system with twice as fast disk ( DISK*2), and on a system that has enough memory to hold all the working set of the application ( ALL_MAIN_MEMORY). The predicted execution times, along with the measured execution times of DISK and REMOTE_MEMORY are plotted in figure 4. We see that ETHERNET*10 performs very close to ALL_MAIN_MEMORY, and significantly better than both REMOTE_MEMORY and DISK.

To understand the results shown in figure 4, we analyze the execution time of FFT with 28Mbytes of input. The measured elapsed time is 208 seconds, consisting of 78.5 sec of useful user time, 5 sec of system time, and 124 sec of network blocking time, spent waiting for pages to go through the Ethernet. During the same run, the application suffered 6520 page-outs and 7791 page-ins. The average waiting time for a page transfer (both for page ins and page outs) on top of the Ethernet is , or about 8.6 ms. Using a ten times faster interconnection network, the average waiting time will be reduced at least to 0.86 ms. Thus, the total completion time of FFT would be at most sec, divided as follows: 82% in user time, 5% in system time, and 13% in network blocking time. We see that a 100 Mbit/sec interconnection network reduces the total paging overhead to a mere 17% of the total applications execution time. We believe that most users would be willing to pay such an overhead in order to run an application that does not fit in main memory.

  
Figure: Performance of FFT for various Architecture Alternatives. DISK is the measured completion time when paging to a local disk. REMOTE_MEMORY is the measured completion time when paging to remote memory on top of the Ethernet. ETHERNET*2 and ETHERNET*10 is the predicted completion time when using remote memory as a paging device, on top of a network that is twice and ten times as fast as the Ethernet interconnection network. DISK*2 is the predicted completion time when using a twice as fast disk for paging. ALL_MAIN_MEMORY is the predicted completion time of FFT when we use the same workstation but with enough memory to hold its entire working set.



next up previous
Next: The Latency of Up: Performance Results Previous: Scaling the Input



Evangelos Markatos
Fri Mar 24 14:41:51 EET 1995