>>> Does this mean I am going to add a >>> new object into M5(write C++ code for perfect_branch_predictor, then >>> instantiate it using python)? I'm thinking you would have to do write a "perfect_branch_predictor" object to plug in.
The only complication is you have to figure out how to resolve the branches of speculative instructions if you are using that for an out-of-order machine. If your workload is small enough, an idea may be figuring out how to pre-execute your workload and save the the branch history so as to use as input to your perfect predictor. >>> How about a perfect L2 cache? Should I just increase the L2 cache >>> size to say 100MB to reduce the miss rate? I'm not sure exactly how this would work, but you might be able to implement just functional accesses to the L1 cache to simulate having a 0% miss rate. Doing that for the L2 cache might be a little more handiwork though I assume... -- ---------- Korey L Sewell Graduate Student - PhD Candidate Computer Science & Engineering University of Michigan _______________________________________________ m5-users mailing list m5-users@m5sim.org http://m5sim.org/cgi-bin/mailman/listinfo/m5-users
