Cache Equalizer: A Cache Pressure Aware Block Placement Scheme for Large-Scale Chip Multiprocessors

This paper describes Cache Equalizer (CE), a novel distributed cache management scheme for large scale chip multiprocessors (CMPs). Our work is motivated by large asymmetry in cache sets usages. CE decouples the physical locations of cache blocks from their addresses for the sake of reducing misses caused by destructive interferences. Temporal pressure at the on-chip last-level cache, is continuously collected at a group (comprised of cache sets) granularity, and periodically recorded at the memory controller to guide the placement process. An incoming block is consequently placed at a cache group that exhibits the minimum pressure. CE provides Quality of Service (QoS) by robustly offering better performance than the baseline shared NUCA cache. Simulation results using a full-system simulator demonstrate that CE outperforms shared NUCA caches by an average of 15.5% and by as much as 28.5% for the benchmark programs we examined. Furthermore, evaluations manifested the outperformance of CE versus related CMP cache designs

A comparison of software and hardware synchronization mechanisms for distributed shared memory multiprocessors

technical reportEfficient synchronization is an essential component of parallel computing. The designers of traditional multiprocessors have included hardware support only for simple operations such as compare-and-swap and load-linked/store-conditional, while high level synchronization primitives such as locks, barriers, and condition variables have been implemented in software [9,14,15]. With the advent of directory-based distributed shared memory (DSM) multiprocessors with significant flexibility in their cache controllers [7,12,17], it is worthwhile considering whether this flexibility should be used to support higher level synchronization primitives in hardware. In particular, as part of maintaining data consistency, these architectures maintain lists of processors with a copy of a given cache line, which is most of the hardware needed to implement distributed locks. We studied two software and four hardware implementations of locks and found that hardware implementation can reduce lock acquire and release times by 25-94% compared to well tuned software locks. In terms of macrobenchmark performance, hardware locks reduce application running times by up to 75% on a synthetic benchmark with heavy lock contention and by 3%-6% on a suite of SPLASH-2 benchmarks. In addition, emerging cache coherence protocols promise to increase the time spent synchronizing relative to the time spent accessing shared data, and our study shows that hardware locks can reduce SPLASH-2 execution times by up to 10-13% if the time spent accessing shared data is small. Although the overall performance impact of hardware lock mechanisms varies tremendously depending on the application, the added hardware complexity on a flexible architecture like FLASH [12] or Avalanche [7] is negligible, and thus hardware support for high level synchronization operations should be provided

A Case for Fine-Grain Adaptive Cache Coherence

As transistor density continues to grow geometrically, processor manufacturers are already able to place a hundred cores on a chip (e.g., Tilera TILE-Gx 100), with massive multicore chips on the horizon. Programmers now need to invest more effort in designing software capable of exploiting multicore parallelism. The shared memory paradigm provides a convenient layer of abstraction to the programmer, but will current memory architectures scale to hundreds of cores? This paper directly addresses the question of how to enable scalable memory systems for future multicores. We develop a scalable, efficient shared memory architecture that enables seamless adaptation between private and logically shared caching at the fine granularity of cache lines. Our data-centric approach relies on in hardware runtime profiling of the locality of each cache line and only allows private caching for data blocks with high spatio-temporal locality. This allows us to better exploit on-chip cache capacity and enable low-latency memory access in large-scale multicores

Evaluating the potential of programmable multiprocessor cache controllers

technical reportThe next generation of scalable parallel systems (e.g., machines by KSR, Convex, and others) will have shared memory supported in hardware, unlike most current generation machines (e.g., offerings by Intel, nCube, and Thinking Machines). However, current shared memory architectures are constrained by the fact that their cache controllers are hardwired and inflexible, which limits the range of programs that can achieve scalable performance. This observation has led a number of researchers to propose building programmable multiprocessor cache controllers that can implement a variety of caching protocols, support multiple communication paradigms, or accept guidance from software. To evaluate the potential performance benefits of these designs, we have simulated five SPLASH benchmark programs on a virtual multiprocessor that supports five directory-based caching protocols. When we compared the off-line optimal performance of this design, wherein each cache line was maintained using the protocol that required the least communication, with the performance achieved when using a single protocol for all lines, we found that use of the "optimal" protocol reduced consistency traffic by 10-80%, with a mean improvement of 25-35%. Cache miss rates also dropped by up to 25%. Thus, the combination of programmable (or tunable) hardware and software able to exploit this added flexibility, e.g., via user pragmas or compiler analysis, could dramatically improve the performance of future shared memory multiprocessors

Jigsaw: Scalable software-defined caches

Shared last-level caches, widely used in chip-multi-processors (CMPs), face two fundamental limitations. First, the latency and energy of shared caches degrade as the system scales up. Second, when multiple workloads share the CMP, they suffer from interference in shared cache accesses. Unfortunately, prior research addressing one issue either ignores or worsens the other: NUCA techniques reduce access latency but are prone to hotspots and interference, and cache partitioning techniques only provide isolation but do not reduce access latency.United States. Defense Advanced Research Projects Agency (DARPA PERFECT contract HR0011-13-2-0005)Quanta Computer (Firm