Cache only memory architectures (COMA) have an inherent memory overhead due to the organization of main memory as a large cache called an attraction memory. This overhead consists of memory left unallocated for performance reasons as well as additional physical memory required due to the cache organization of memory. The authors examine the effect of data reshuffling and data replication on the memory overhead. Data reshuffling occurs when space needs to be allocated to store a remote memory line in the local memory. Data that is reshuffled is sent between memories via replacement messages. A simple mathematical model predicts the frequency of data reshuffling as a function of the attraction memory parameters. Simulation data shows that the frequency of data reshuffling is sensitive to the allocation policy and associativity of the memory but is relatively unaffected by the block size chosen. The simulation data also shows that data replication in the attraction memory is important for good performance, but most gains can be achieved through replication in the processor caches.>

Two interesting variants of large-scale shared-addressspace parallel architectures are cache-coherent non-uru~ormmemory-access machines (CC-NUMA) and cache-only memory architectures (COMA).Both have distributed main memory and use directory-based cache coherence.While both architectures m-grate and replicate data at the cache level automatically under hardware control, COIUA machines do this at the main memory level as well.Previous work had discussed the general advantages and disadvantages of the two opes of architectures, and presented results comparing the performance of small problems on simulated architectures of the two types.In this piper, we compare the parallel performance of a recent realizatwn of each type of architecturuhe Stanford DASH multiprocessor (CC-NUMA) and the Kendall Square Research KSR-1 (COMA).Using a suite of important computatwrtai kernels and complete scientific applicatwns, we examt"ne performance dl~erences resulting both from the CC-NUMAICOMA nature of the machines as well as from spectfii dt~erences in system implementation.

The fundamental premise behind the DASH project is that it is feasible to build large-scale shared-memory multiprocessors with hardware cache coherence. The hardware overhead of directory-based cache coherence in a 48-processor is examined. The data show that the overhead is only about 10-15%, which appears to be a small cost for the ease of programming offered by coherent caches and the potential for higher performance. The performance of the system is discussed, and the speedups obtained by a variety of parallel applications running on the prototype are shown. Using a sophisticated hardware performance monitor, the effectiveness of coherent caches and the relationship between an application's reference behavior and its speedup are characterized. The optimizations incorporated in the DASH protocol are evaluated in terms of their effectiveness on parallel applications and on atomic tests that stress the memory system.>

The fundamental premise behind the DASH project is that it is feasible to build large-scale shared-memory multiprocessors with hardware cache coherence. While paper studies and software simulators are useful for understanding many high-level design trade-offs, prototypes are essential to ensure that no critical details are overlooked. A prototype provides convincing evidence of the feasibility of the design allows one to accurately estimate both the hardware and the complexity cost of various features, and provides a platform for studying real workloads. A 16-processor prototype of the DASH multiprocessor has been operational for the last six months. In this paper, the hardware overhead of directory-based cache coherence in the prototype is examined. We also discuss the performance of the system, and the speedups obtained by parallel applications running on the prototype. Using a sophisticated hardware performance monitor, we characterize the effectiveness of coherent caches and the relationship between an application's reference behavior and its speedup.

Two interesting variations of large-scale shared-memory machines that have recently emerged are cache-coherent non-uniform-memory-access machines (CC-NUMA) and cache-only memory architectures (COMA). They both have distributed main memory and use directory-based cache coherence. Unlike CC-NUMA, however, COMA machines automatically migrate and replicate data at the main-memory level in cache-line sized chunks. This paper compares the performance of these two classes of machines. We first present a qualitative model that shows that the relative performance is primarily determined by two factors: the relative magnitude of capacity misses versus coherence misses, and the granularity of data partitions in the application. We then present quantitative results using simulation studies for eight parallel applications (including all six applications from the SPLASH benchmark suite). We show that COMA's potential for performance improvement is limited to applications where data accesses by different processors are finely interleaved in memory space and, in addition, where capacity misses dominate over coherence misses. In other situations, for example where coherence misses dominate, COMA can actually perform worse than CC-NUMA due to increased miss latencies caused by its hierarchical directories. Finally, we propose a new architectural alternative, called COMA-F, that combines the advantages of both CC-NUMA and COMA.