The growing disparity between processor and memory performance has made cache misses increasingly expensive. Additionally, data and instruction caches are not always used efficiently, resulting in large numbers of cache misses. Therefore, the importance of cache performance improvements at each level of the memory hierarchy will continue to grow. In numeric programs, there are several known compiler techniques for optimizing data cache performance. However, integer (nonnumeric) programs often have irregular access patterns that are more difficult for the compiler to optimize. In the past, cache management techniques such as cache bypassing were implemented manually at the machine-language-programming level. As the available chip area grows, it makes sense to spend more resources to allow intelligent control over the cache management. In this paper, we present an approach to improving cache effectiveness, taking advantage of the growing chip area, utilizing run-time adaptive cache management techniques, optimizing both performance and cost of implementation. Specifically, we are aiming to increase data cache effectiveness for integer programs. We propose a microarchitecture scheme where the hardware determines data placement within the cache hierarchy based on dynamic referencing behavior. This scheme is fully compatible with existing instruction set architectures. This paper examines the theoretical upper bounds on the cache hit ratio that cache bypassing can provide for integer applications, including several Windows applications with OS activity. Then, detailed trace-driven simulations of the integer applications are used to show that the implementation described in this paper can achieve performance close to that of the upper bound.

Improvements in main memory speeds have not kept pace with increasing processor clock frequency and improved exploitation of instruction-level parallelism. Consequently, the gap between processor and main memory performance is expected to grow, increasing the number of execution cycles spent waiting for memory accesses to complete. One solution to this growing problem is to reduce the number of cache misses by increasing the effectiveness of the cache hierarchy. In this paper we present a technique for dynamic analysis of program data access behavior, which is then used to proactively guide the placement of data within the cache hierarchy in a location-sensitive manner. We introduce the concept of a macroblock, which allows us to feasibly characterize the memory locations accessed by a program, and a Memory Address Table, which performs the dynamic reference analysis. Our technique is fully compatible with existing Instruction Set Architectures. Results from detailed simulations of several integer programs show significant speedups.

As the disparity between processor and main memory performance grows, the number of execution cycles spent waiting for memory accesses to complete also increases. As a result, latency hiding techniques are critical for improved application performance on future processors. We present a microarchitecture scheme which detects and adapts to varying spatial locality, dynamically adjusting the amount of data fetched on a cache miss. The spatial locality detection table, introduced in this paper, facilitates the detection of spatial locality across adjacent cached blocks. Results from detailed simulations of several integer programs show significant speedups. The improvements are due to the reduction of conflict and capacity misses by utilizing small blocks and small fetch sizes when, spatial locality is absent, and the prefetching effect of large fetch sizes when spatial locality exists.