False sharing

Summary

In computer science, false sharing is a performance-degrading usage pattern that can arise in systems with distributed, coherent caches at the size of the smallest resource block managed by the caching mechanism. When a system participant attempts to periodically access data that is not being altered by another party, but that data shares a cache block with data that is being altered, the caching protocol may force the first participant to reload the whole cache block despite a lack of logical necessity. The caching system is unaware of activity within this block and forces the first participant to bear the caching system overhead required by true shared access of a resource. Multiprocessor CPU caches By far the most common usage of this term is in modern multiprocessor CPU caches, where memory is cached in lines of some small power of two word size (e.g., 64 aligned, contiguous bytes). If two processors operate on independent data in the same memory address region storable

Official source

https://en.wikipedia.org/wiki/False_sharing

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TreadMarks is a distributed shared memory (DSM) system for standard Unix systems such as SunOS and Ultrix. This paper presents a performance evaluation of TreadMarks running on Ultrix using DECstation-5000/240's that are connected by a 100-Mbps switch-based ATM LAN and a 10-Mbps Ethernet. Our objective is to determine the efficiency of a user-level DSM implementation on commercially available workstations and operating systems. We achieved good speedups on the 8-processor ATM network for Jacobi (7.4), TSP (7.2), Quicksort (6.3), and ILINK (5.7). For a slightly modified version ofWater from the SPLASH benchmark suite, we achieved only moderate speedups (4.0) due to the high communication and synchronization rate. Speedups decline on the 10-Mbps Ethernet (5.5 for Jacobi, 6.5 for TSP, 4.2 for Quicksort, 5.1 for ILINK, and 2.1 for Water), re ecting the bandwidth limitations of the Ethernet. These results support the contention that, with suitable networking technology, DSM is a viable technique for parallel computation on clusters of workstations. To achieve these speedups, TreadMarks goes to great lengths to reduce the amount of communication performed to maintain memory consistency. It uses a lazy implementation of release consistency, and it allows multiple concurrent writers to modify a page, reducing the impact of false sharing. Great care was taken to minimize communication overhead. In particular, on the ATM network, we used a standard low-level protocol, AAL3/4, bypassing the TCP/IP protocol stack. Unix communication overhead, however, remains the main obstacle in the way of better performance for programs like Water. Compared to the Unix communication overhead, memory management cost (both kernel and user level) is small and wire time is negligible.

1994

Quantifying the Performance Differences Between PVM and TreadMarks

Sandhya Dwarkadas, Willy Zwaenepoel

We compare two systems for parallel programming on networks of workstations: Parallel Virtual Machine (PVM) a message passing system, and TreadMarks, a software distributed shared memory (DSM) system. We present results for eight applications that were implemented using both systems. The programs are Water and Barnes-Hut from the SPLASH benchmark suite; 3-D FFT, Integer Sort (IS) and Embarrassingly Parallel (EP) from the NAS benchmarks; ILINK, a widely used genetic linkage analysis program; and Successive Over-Relaxation (SOR) and Traveling Salesman (TSP). Two different input data sets were used for five of the applications. We use two execution environments. The first is an 155 Mbps ATM network with eight Sparc-20 model 61 workstations; the second is an eight processor IBM SP/2. The differences in speedup between TreadMarks and PVM are dependent on the application, and, only to much a lesser extent, on the platform and the data set used. In particular, the TreadMarks speedup for six of the eight applications is within 15% of that achieved with PVM. For one application, the difference in speedup is between 15% and 30%, and for one application, the difference is around 50%. More important than the actual differences in speedups, we investigate the causes behind these differences. The cost of sending and receiving messages on current networks of workstations is very high, and previous work has identified communication costs as the primary source of overhead in software DSM implementations. The observed performance differences between PVM and TreadMarks are therefore primarily a result of differences in the amount of communication between the two systems. We identified four factors that contribute to the larger amount of communication in TreadMarks:1) extra messages due to the separation of synchronization and data transfer, 2) extra messages to handle access misses caused by the use of an invalidate protocol, 3) false sharing, and 4) d iff accumulation for migratory data. We have quantified the effect of the last three factors by measuring the performance gain when each is eliminated. Because the separation of synchronization and data transfer is a fundamental characteristic of the shared memory model, there is no way to measure its contribution to performance without completely deviating from the shared memory model. Of the three remaining factors, TreadMarks’ inability to send data belonging to different pages in a single message is the most important. The effect of false sharing is quite limited. Reducing diff accumulation benefits migratory data only when the diffs completely overlap. When these performance impediments are removed, all of the TreadMarks programs perform within 25% of PVM, and for six out of eight experiments, TreadMarks is less than 5% slower than PVM.

1997

The message passing programs are executed with the Parallel Virtual Machine (PVM) library and the shared memory programs are executed using TreadMarks. The programs are Water and Barnes-Hut from the SPLASH benchmark suite; 3-D FFT, Integer Sort (IS) and Embarassingly Parallel (EP) from the NAS benchmarks; ILINK, a widely used genetic linkage analysis program; and Successive Over-Relaxation (SOR), Traveling Salesman (TSP), and Quicksort (QSORT). Two different input data sets were used for Water (Water-288 and Water-1728), IS (IS-Small and IS-Large), and SOR (SOR-Zero and SOR-NonZero). Our execution environment is a set of eight HP735 workstations connected by a 100Mbits per second FDDI network. For Water-1728, EP, ILINK, SOR-Zero, and SOR-NonZero, the performance of TreadMarks is within 10% of PVM. For IS-Small, Water-288, Barnes-Hut, 3-D FFT, TSP, and QSORT, differences are on the order of 10% to 30%. Finally, for IS-Large, PVM performs two times better than TreadMarks. More messages and more data are sent in TreadMarks, explaining the performance differences. This extra communication is caused by 1) the separation of synchronization and data transfer, 2) extra messages to request updates for data by the invalidate protocol used in TreadMarks, 3) false sharing, and 4) diff accumulation for migratory data in TreadMarks.

1995

Quantifying the Performance Differences Between PVM and TreadMarks

Sandhya Dwarkadas, Willy Zwaenepoel

1997