Memory access pattern

Résumé

In computing, a memory access pattern or IO access pattern is the pattern with which a system or program reads and writes memory on secondary storage. These patterns differ in the level of locality of reference and drastically affect cache performance, and also have implications for the approach to parallelism and distribution of workload in shared memory systems. Further, cache coherency issues can affect multiprocessor performance, which means that certain memory access patterns place a ceiling on parallelism (which manycore approaches seek to break). Computer memory is usually described as "random access", but traversals by software will still exhibit patterns that can be exploited for efficiency. Various tools exist to help system designers and programmers understand, analyse and improve the memory access pattern, including VTune and Vectorization Advisor, including tools to address GPU memory access patterns Memory access patterns also have implications for security, which mot

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

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While DRAM latency has long been recognized as a major bottleneck in servers, DRAM bandwidth is emerging as an important bottleneck as server processors shift to many-core architectures to allow for sustainable throughput improvements. The rapid expansion of the digital universe, increasingly stored in memory, rapidly pushes the need for higher DRAM density as well. Emerging die-stacked DRAM technology dramatically improves the three major DRAM properties: latency, bandwidth and density. Recent advancements in die-stacking technology made it possible to integrate a sizeable amount of DRAM directly on top of the processor. While the feasible on-chip DRAM capacities are insufficient to satisfy the memory needs of modern servers, architecting on-chip DRAM as a high-capacity low-latency high-bandwidth cache has the potential to provide significant reduction both in off-chip memory traffic and in average memory access latency. We make the observation that high-capacity on-chip DRAM caches expose abundant spatial locality present in server applications and a modest amount of temporal data reuse. As a consequence, DRAM caches that manage and fetch data at a coarser granularity, e.g., in 2KB pages, exhibit overall superior properties compared to caches that do fine-grain management using 64B blocks. These properties include substantially higher hit rates, smaller tag storage, higher energy efficiency and set-associativity. Unfortunately, naive employment of page-based caches results in excessive data overfetch and capacity waste, as some of the fetched and allocated blocks are never accessed prior to their eviction. We demonstrate that if the cache is organized in pages, then page footprints -- i.e., the set of blocks that are touched while the page is in the cache -- are highly predictable using well-established code-correlation techniques. Accurately predicting access patterns within a page can eliminate most of the bandwidth overhead and capacity waste that page-based caches suffer from.

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Dynamic Resource Allocation for Database Servers Running on Virtual Storage

Jin Chen, Daniel Lupei, Adrian Daniel Popescu

We introduce a novel multi-resource allocator to dynamically allocate resources for database servers running on virtual storage. Multi-resource allocation involves proportioning the database and storage server caches, and the storage bandwidth between applications according to overall performance goals. The problem is challenging due to the interplay between different resources, e.g., changing any cache quota affects the access pattern at the cache/disk levels below it in the storage hierarchy. We use a combination of on-line modeling and sampling to arrive at near-optimal configurations within minutes. The key idea is to incorporate access tracking and known resource dependencies e.g., due to cache replacement policies, into our performance model. In our experimental evaluation, we use both microbenchmarks and the industry standard benchmarks TPC-W and TPC-C. We show that our multi-resource allocation approach improves application performance by up to factors of 2.9 and 2.4 compared to state-of-the-art single-resource controllers, and their ad-hoc combination, respectively.

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FPGAs for the Masses: Affordable Hardware Synthesis from Domain-Specific Languages

Nithin George

Field Programmable Gate Arrays (FPGAs) have the unique ability to be configured into application-specific architectures that are well suited to specific computing problems. This enables them to achieve performances and energy efficiencies that outclass other processor-based architectures, such as Chip Multiprocessors (CMPs), Graphic Processing Units (GPUs) and Digital Signal Processors (DSPs). Despite this, FPGAs are yet to gain widespread adoption, especially among application and software developers, because of their laborious application development process that requires hardware design expertise. In some application areas, domain-specific hardware synthesis tools alleviate this problem by using a Domain-Specific Language (DSL) to hide the low-level hardware details and also improve productivity of the developer. Additionally, these tools leverage domain knowledge to perform optimizations and produce high-quality hardware designs. While this approach holds great promise, the significant effort and cost of developing such domain-specific tools make it unaffordable in many application areas. In this thesis, we develop techniques to reduce the effort and cost of developing domain-specific hardware synthesis tools. To demonstrate our approach, we develop a toolchain to generate complete hardware systems from high-level functional specifications written in a DSL. Firstly, our approach uses language embedding and type-directed staging to develop a DSL and compiler in a cost-effective manner. To further reduce effort, we develop this compiler by composing reusable optimization modules, and integrate it with existing hardware synthesis tools. However, most synthesis tools require users to have hardware design knowledge to produce high-quality results. Therefore, secondly, to facilitate people without hardware design skills to develop domain-specific tools, we develop a methodology to generate high-quality hardware designs from well known computational patterns, such as map, zipWith, reduce and foreach; computational patterns are algorithmic methods that capture the nature of computation and communication and can be easily understood and used without expert knowledge. In our approach, we decompose the DSL specifications into constituent computational patterns and exploit the properties of these patterns, such as degree of parallelism, interdependence between operations and data-access characteristics, to generate high-quality hardware modules to implement them, and compose them into a complete system design. Lastly, we extended our methodology to automatically parallelize computations across multiple hardware modules to benefit from the spatial parallelism of the FPGA as well as overcome performance problems caused by non-sequential data access patterns and long access latency to external memory. To achieve this, we utilize the data-access properties of the computational patterns to automatically identify synchronization requirements and generate such multi-module designs from the same high-level functional specifications. Driven by power and performance constraints, today the world is turning to reconfigurable technology (i.e., FPGAs) to meet the computational needs of tomorrow. In this light, this work addresses the cardinal problem of making tomorrow's computing infrastructure programmable to application developers.

EPFL2016

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FPGAs for the Masses: Affordable Hardware Synthesis from Domain-Specific Languages

Nithin George

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