Estimating interlock and improving balance for pipelined architectures
Journal of Parallel and Distributed Computing
Dynamic hot data stream prefetching for general-purpose programs
PLDI '02 Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation
Cache performance for selected SPEC CPU2000 benchmarks
ACM SIGARCH Computer Architecture News
The Memory Bandwidth Bottleneck and its Amelioration by a Compiler
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
A performance-correctness explicitly-decoupled architecture
Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
Pinpointing data locality problems using data-centric analysis
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
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A "hot" concept in program optimization is hotness. For example, program optimization targets hot paths, and register allocation targets hot variables. Cache optimization, however, has to target cold data, which are less frequently used and tend to cause cache misses whenever they are accessed. Hot data, in contrast, as they are small and frequently used, tend to stay in cache. In this paper, we define a new metric called "coldness" and show how the coldness varies across programs and how much colder the data we have to optimize as the cache size on modern machines increases.