Address-value delta (AVD) prediction: increasing the effectiveness of runahead execution by exploiting regular memory allocation patterns

While runahead execution is effective at parallelizing independent long-latency cache misses, it is unable to parallelize dependent long-latency cache misses. To overcome this limitation, this paper proposes a novel technique, address-value delta (AVD) prediction. An AVD predictor keeps track of the address (pointer) load instructions for which the arithmetic difference (i.e., delta) between the effective address and the data value is stable. If such a load instruction incurs a long-latency cache miss during runahead execution, its data value is predicted by subtracting the stable delta from its effective address. This prediction enables the pre-execution of dependent instructions, including load instructions that incur long-latency cache misses. We describe how, why, and for what kind of loads AVD prediction works and evaluate the design tradeoffs in an implementable AVD predictor. Our analysis shows that stable AVDs exist because of patterns in the way data structures are allocated in memory. Our results show that augmenting a runahead processor with a simple, 16-entry AVD predictor improves the average execution time of a set of pointer-intensive applications by 12.1%.