As shown in Figure 3, performing speculative execution significantly reduces the execution times of our benchmark applications (by 69%, 29% and 70%, respectively, for Agrep, Gnuld and XDataSlice). For Agrep and XDataSlice, we were able to automatically achieve the same performance improvements obtained when the applications were manually modified to issue hints. For Gnuld, our gain was much less than that of the manually modified application, but still represents a substantial improvement over the original non-hinting application. Based on these results, and considering the relative unsophistication of our tool, speculative execution promises to be an effective technique for exploiting disk parallelism and underutilized processor cycles to reduce the execution time of disk-bound applications.
Figure 3: Performance improvement.
Original corresponds to the original, non-hinting applications;
Speculating corresponds to the applications transformed to perform
speculative execution for hint generation; and Manual corresponds
to the applications manually modified to issue hints. In all cases, the
non-hinted read calls issued by the applications invoked the operating
system's sequential read-ahead policy (which is described at the beginning
of Section 4).
If TIP is configured to ignore hints, the applications that perform speculative execution were no more than 4%, and as little as 1%, slower than the original applications as shown in Figure 4. These figures capture all of the factors that can contribute to the worst-case performance of the speculating applications except the potential negative effects of any erroneous hints. These factors include increased memory contention, the overhead of checking hint log entries before issuing read calls, and the overhead of executing an initialization routine that, among other things, spawns the speculating thread.
Figure 4: Runtime overhead of supporting speculative
execution, as captured by running the benchmarks with TIP configured to
ignore hints.