Conflict-free strides for vectors in matched memories

Address transformation schemes, such as skewing and linear transformations, have been proposed to achieve conflict-free access to one family of strides in vector processors with matched memories. The paper extends these schemes to achieve this conflict-free access for several families. The basic idea is to perform an out-of-order access to vectors of fixed length, equal to that of the vector registers of the processor. The hardware required is similar to that for the access in order.Peer ReviewedPostprint (author's final draft

Bank-interleaved cache or memory indexing does not require euclidean division

International audience! Abstract Concurrent access to bank-interleaved memory structure have been studied for decades, particularly in the context of vector supercomputer systems. It is still common belief that using a number of banks different from 2 n leads to insert a complex hardware including a non-trivial divider on the access path to the memory. In 1993, two independent studies [1], [2] were showing that through leveraging a very simple arithmetic result, the Chinese Remainder Theorem, this euclidean division is not needed when the number of banks is prime or simply odd. In the mid 90's, the interest for vector supercomputers faded and the research topic disappeared. The interest for bank-interleaved cache has reappeared recently [3] in the GPU context. In this short paper, we extend the result from [1] and we show that, regardless the number of banks: Bank-interleaved cache or memory indexing does not require euclidean division

Future vector microprocessor extensions for data aggregations

As the rate of annual data generation grows exponentially, there is a demand to aggregate and summarise vast amounts of information quickly. In the past, frequency scaling was relied upon to push application throughput. Today, Dennard scaling has ceased and further performance must come from exploiting parallelism. Single instruction-multiple data (SIMD) instruction sets offer a highly efficient and scalable way of exploiting data-level parallelism (DLP). While microprocessors originally offered very simple SIMD support targeted at multimedia applications, these extensions have been growing both in width and functionality. Observing this trend, we use a simulation framework to model future SIMD support and then propose and evaluate five different ways of vectorising data aggregation. We find that although data aggregation is abundant in DLP, it is often too irregular to be expressed efficiently using typical SIMD instructions. Based on this observation, we propose a set of novel algorithms and SIMD instructions to better capture this irregular DLP. Furthermore, we discover that the best algorithm is highly dependent on the characteristics of the input. Our proposed solution can dynamically choose the optimal algorithm in the majority of cases and achieves speedups between 2.7x and 7.6x over a scalar baseline.The research leading to these results has received funding from the RoMoL ERC Advanced Grant GA no 321253 and is supported in part by the European Union (FEDER funds) under contract TTIN2015-65316-P. Timothy Hayes is supported by a FPU research grant from the Spanish MECD.Peer ReviewedPostprint (published version

Using Prime Numbers for Cache Indexing to Eliminate Conflict Misses, HPCA

Using alternative cache indexing/hashing functions is a popular technique to reduce conflict misses by achieving a more uniform cache access distribution across the sets in the cache. Although various alternative hashing functions have been demonstrated to eliminate the worst case conflict behavior, no study has really analyzed the pathological behavior of such hashing functions that often result in performance slowdown. In this paper, we present an in-depth analysis of the pathological behavior of cache hashing functions. Based on the analysis, we propose two new hashing functions: prime modulo and prime displacement that are resistant to pathological behavior and yet are able to eliminate the worst case conflict behavior in the L2 cache. We show that these two schemes can be implemented in fast hardware using a set of narrow add operations, with negligible fragmentation in the L2 cache. We evaluate the schemes on 23 memory intensive applications. For applications that have non-uniform cache accesses, both prime modulo and prime displacement hashing achieve an average speedup of 1.27 compared to traditional hashing, without slowing down any of the 23 benchmarks. We also evaluate using multiple prime displacement hashing functions in conjunction with a skewed associative L2 cache. The skewed associative cache achieves a better average speedup at the cost of some pathological behavior that slows down four applications by up to 7%. 1

Reducing dram access latency by exploiting dram leakage characteristics and common access patterns

DRAM tabanlı bellek, bilgisayar sisteminde darboğaz oluşturarak sistemin başarımı sınırlayan en önemli bileşendir. Bunun sebebi işlemcilerin hız bakımından DRAM'lerin çok önünde olmasıdır. Bu tezde, ChargeCache ismini verdiğimiz, DRAM'lerin erişim gecikmesini azaltan bir yöntem geliştirdik. Bu yöntem, piyasadaki DRAM yongalarının mimarisinde bir değişiklik gerektirmediği gibi, bellek denetimcisinde de düşük donanım maliyeti olan ek birimlere ihtiyaç duymaktadır. ChargeCache, yeni erişilmiş DRAM satırlarının kısa bir süre sonra tekrar erişileceği gözlemine dayanmaktadır. Yeni erişilmiş satırlardaki DRAM hücreleri yüksek miktarda yük içerdiğinden, bunlara hızlı bir şekilde erişilebilir. Bu gözlemden faydalanmak için yeni erişilen satırların adreslerini bellek denetimcisi içerisinde bir tabloda tutmayı öneriyoruz. Sonraki erişim isteklerinin bu tablodaki satırlara erişmek istemesi durumunda, bellek denetimcisi yük miktarı yüksek hücrelerin erişilmek üzere olduğunu bileceğinden, DRAM erişim değiştirgelerini ayarlayarak erişimin düşük gecikmeyle tamamlanmasını sağlayabilir. Belirli bir süre sonra tablodaki satır adresleri silinerek, zaman içerisinde çok fazla yük kaybedip hızlı erişilebilme özelliğini yitirmiş satırların bu tablodan çıkarılması sağlanır. Önerdiğimiz yöntemi hem tek çekirdekli hem de çok çekirdekli mimarilerde benzetim ortamında deneyerek, yöntemin başarım ve enerji kullanımı açısından sistem üzerinde sağladığı iyileştirmeleri inceledik.DRAM-based memory is a critical factor that creates a bottleneck on the system performance since the processor speed largely outperforms the DRAM latency. In this thesis, we develop a low-cost mechanism, called ChargeCache, which enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems

Improving the Performance and Energy Efficiency of GPGPU Computing through Adaptive Cache and Memory Management Techniques

Department of Computer Science and EngineeringAs the performance and energy efficiency requirement of GPGPUs have risen, memory management techniques of GPGPUs have improved to meet the requirements by employing hardware caches and utilizing heterogeneous memory. These techniques can improve GPGPUs by providing lower latency and higher bandwidth of the memory. However, these methods do not always guarantee improved performance and energy efficiency due to the small cache size and heterogeneity of the memory nodes. While prior works have proposed various techniques to address this issue, relatively little work has been done to investigate holistic support for memory management techniques. In this dissertation, we analyze performance pathologies and propose various techniques to improve memory management techniques. First, we investigate the effectiveness of advanced cache indexing (ACI) for high-performance and energy-efficient GPGPU computing. Specifically, we discuss the designs of various static and adaptive cache indexing schemes and present implementation for GPGPUs. We then quantify and analyze the effectiveness of the ACI schemes based on a cycle-accurate GPGPU simulator. Our quantitative evaluation shows that ACI schemes achieve significant performance and energy-efficiency gains over baseline conventional indexing scheme. We also analyze the performance sensitivity of ACI to key architectural parameters (i.e., capacity, associativity, and ICN bandwidth) and the cache indexing latency. We also demonstrate that ACI continues to achieve high performance in various settings. Second, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. Based on the performance pathology analysis of GPGPUs, we integrate state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework to eliminate performance pathologies. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1% and 61.9% on average, respectively) and achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4% at maximum and 7.7% on average). Furthermore, IACM delivers significant performance and energy efficiency gains over the baseline GPGPU architecture even when enhanced with advanced architectural technologies (e.g., higher capacity, associativity). Third, we propose bandwidth- and latency-aware page placement (BLPP) for GPGPUs with heterogeneous memory. BLPP analyzes the characteristics of a application and determines the optimal page allocation ratio between the GPU and CPU memory. Based on the optimal page allocation ratio, BLPP dynamically allocate pages across the heterogeneous memory nodes. Our experimental results show that BLPP considerably outperforms the baseline and state-of-the-art technique (i.e., 13.4% and 16.7%) and performs similar to the static-best version (i.e., 1.2% difference), which requires extensive offline profiling.clos