Although definition of single-program benchmarks is relatively straight-forward-a benchmark is a program plus a specific input-definition of multi-program benchmarks is more complex. Each program may have a different runtime and they may have different interactions depending on how they align with each other. While prior work has focused on sampling multiprogram benchmarks, little attention has been paid to defining the benchmarks in their entirety. In this work, we propose a four-tuple that formally defines multi-program benchmarks in a well-defined way. We then examine how four different classes of benchmarks created by varying the elements of this tuple align with real-world use-cases. We evaluate the impact of these variations on real hardware, and see drastic variations in results between different benchmarks constructed from the same programs. Notable differences include significant speedups versus slowdowns (e.g., +57% vs -5% or +26% vs -18%), and large differences in magnitude even when the results are in the same direction (e.g., 67% versus 11%).

Some recent memory technologies, including phase change memory (PCM), have lifetime reliabilities that are affected by write operations. We propose the use of coset coding to extend the lifetimes of these memories. The key idea of coset coding is that it performs a one-to-many mapping from each dataword to a coset of vectors, and having multiple possible vectors provides the flexibility to choose the vector to write that optimizes lifetime. Our technique, FlipMin, uses coset coding and, for each write, selects the vector that minimizes the number of bits that must flip. We also show how FlipMin can be synergistically combined with the ability to tolerate bit erasures. Thus, our techniques help to prevent bits from wearing out and can then tolerate those bits that do wear out.