Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures

With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications

Talus: A simple way to remove cliffs in cache performance

Caches often suffer from performance cliffs: minor changes in program behavior or available cache space cause large changes in miss rate. Cliffs hurt performance and complicate cache management. We present Talus, a simple scheme that removes these cliffs. Talus works by dividing a single application's access stream into two partitions, unlike prior work that partitions among competing applications. By controlling the sizes of these partitions, Talus ensures that as an application is given more cache space, its miss rate decreases in a convex fashion. We prove that Talus removes performance cliffs, and evaluate it through extensive simulation. Talus adds negligible overheads, improves single-application performance, simplifies partitioning algorithms, and makes cache partitioning more effective and fair.National Science Foundation (U.S.) (Grant CCF-1318384

Adaptive Resource Management Techniques for High Performance Multi-Core Architectures

Reducing the average memory access time is crucial for improving the performance of applications executing on multi-core architectures. With workload consolidation this becomes increasingly challenging due to shared resource contention. Previous works has proposed techniques for partitioning of shared resources (e.g. cache and bandwidth) and prefetch throttling with the goal of mitigating contention and reducing or hiding average memory access time.Cache partitioning in multi-core architectures is challenging due to the need to determine cache allocations with low computational overhead and the need to place the partitions in a locality-aware manner. The requirement for low computational overhead is important in order to have the capability to scale to large core counts. Previous work within multi-resource management has proposed coordinately managing a subset of the techniques: cache partitioning, bandwidth partitioning and prefetch throttling. However, coordinated management of all three techniques opens up new possible trade-offs and interactions which can be leveraged to gain better performance. This thesis contributes with two different resource management techniques: One resource manger for scalable cache partitioning and a multi-resource management technique for coordinated management of cache partitioning, bandwidth partitioning and prefetching. The scalable resource management technique for cache partitioning uses a distributed and asynchronous cache partitioning algorithm that works together with a flexible NUCA enforcement mechanism in order to give locality-aware placement of data and support fine-grained partitions. The algorithm adapts quickly to application phase changes. The distributed nature of the algorithm together with the low computational complexity, enables the solution to be implemented in hardware and scale to large core counts. The multi-resource management technique for coordinated management of cache partitioning bandwidth partitioning and prefetching is designed using the results from our in-depth characterisation from the entire SPEC CPU2006 suite. The solution consists of three local resource management techniques that together with a coordination mechanism provides allocations which takes the inter-resource interactions and trade-offs into account.Our evaluation shows that the distributed cache partitioning solution performs within 1% from the best known centralized solution, which cannot scale to large core counts. The solution improves performance by 9% and 16%, on average, on a 16 and 64-core multi-core architecture, respectively, compared to a shared last-level cache. The multi-resource management technique gives a performance increase of 11%, on average, over state-of-the-art and improves performance by 50% compared to the baseline 16-core multi-core without cache partitioning, bandwidth partitioning and prefetch throttling

Jigsaw: Scalable Software-Defined Caches (Extended Version)

Shared last-level caches, widely used in chip-multiprocessors (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. We present Jigsaw, a technique that jointly addresses the scalability and interference problems of shared caches. Hardware lets software define shares, collections of cache bank partitions that act as virtual caches, and map data to shares. Shares give software full control over both data placement and capacity allocation. Jigsaw implements efficient hardware support for share management, monitoring, and adaptation. We propose novel resource-management algorithms and use them to develop a system-level runtime that leverages Jigsaw to both maximize cache utilization and place data close to where it is used. We evaluate Jigsaw using extensive simulations of 16- and 64-core tiled CMPs. Jigsaw improves performance by up to 2.2x (18% avg) over a conventional shared cache, and significantly outperforms state-of-the-art NUCA and partitioning techniques.This work was supported in part by DARPA PERFECT contract HR0011-13-2-0005 and Quanta Computer

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Intelligent Scheduling and Memory Management Techniques for Modern GPU Architectures

abstract: With the massive multithreading execution feature, graphics processing units (GPUs) have been widely deployed to accelerate general-purpose parallel workloads (GPGPUs). However, using GPUs to accelerate computation does not always gain good performance improvement. This is mainly due to three inefficiencies in modern GPU and system architectures. First, not all parallel threads have a uniform amount of workload to fully utilize GPU’s computation ability, leading to a sub-optimal performance problem, called warp criticality. To mitigate the degree of warp criticality, I propose a Criticality-Aware Warp Acceleration mechanism, called CAWA. CAWA predicts and accelerates the critical warp execution by allocating larger execution time slices and additional cache resources to the critical warp. The evaluation result shows that with CAWA, GPUs can achieve an average of 1.23x speedup. Second, the shared cache storage in GPUs is often insufficient to accommodate demands of the large number of concurrent threads. As a result, cache thrashing is commonly experienced in GPU’s cache memories, particularly in the L1 data caches. To alleviate the cache contention and thrashing problem, I develop an instruction aware Control Loop Based Adaptive Bypassing algorithm, called Ctrl-C. Ctrl-C learns the cache reuse behavior and bypasses a portion of memory requests with the help of feedback control loops. The evaluation result shows that Ctrl-C can effectively improve cache utilization in GPUs and achieve an average of 1.42x speedup for cache sensitive GPGPU workloads. Finally, GPU workloads and the co-located processes running on the host chip multiprocessor (CMP) in a heterogeneous system setup can contend for memory resources in multiple levels, resulting in significant performance degradation. To maximize the system throughput and balance the performance degradation of all co-located applications, I design a scalable performance degradation predictor specifically for heterogeneous systems, called HeteroPDP. HeteroPDP predicts the application execution time and schedules OpenCL workloads to run on different devices based on the optimization goal. The evaluation result shows HeteroPDP can improve the system fairness from 24% to 65% when an OpenCL application is co-located with other processes, and gain an additional 50% speedup compared with always offloading the OpenCL workload to GPUs. In summary, this dissertation aims to provide insights for the future microarchitecture and system architecture designs by identifying, analyzing, and addressing three critical performance problems in modern GPUs.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

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

Doctor of Philosophy

dissertationIn recent years, a number of trends have started to emerge, both in microprocessor and application characteristics. As per Moore's law, the number of cores on chip will keep doubling every 18-24 months. International Technology Roadmap for Semiconductors (ITRS) reports that wires will continue to scale poorly, exacerbating the cost of on-chip communication. Cores will have to navigate an on-chip network to access data that may be scattered across many cache banks. The number of pins on the package, and hence available off-chip bandwidth, will at best increase at sublinear rate and at worst, stagnate. A number of disruptive memory technologies, e.g., phase change memory (PCM) have begun to emerge and will be integrated into the memory hierarchy sooner than later, leading to non-uniform memory access (NUMA) hierarchies. This will make the cost of accessing main memory even higher. In previous years, most of the focus has been on deciding the memory hierarchy level where data must be placed (L1 or L2 caches, main memory, disk, etc.). However, in modern and future generations, each level is getting bigger and its design is being subjected to a number of constraints (wire delays, power budget, etc.). It is becoming very important to make an intelligent decision about where data must be placed within a level. For example, in a large non-uniform access cache (NUCA), we must figure out the optimal bank. Similarly, in a multi-dual inline memory module (DIMM) non uniform memory access (NUMA) main memory, we must figure out the DIMM that is the optimal home for every data page. Studies have indicated that heterogeneous main memory hierarchies that incorporate multiple memory technologies are on the horizon. We must develop solutions for data management that take heterogeneity into account. For these memory organizations, we must again identify the appropriate home for data. In this dissertation, we attempt to verify the following thesis statement: "Can low-complexity hardware and OS mechanisms manage data placement within each memory hierarchy level to optimize metrics such as performance and/or throughput?" In this dissertation we argue for a hardware-software codesign approach to tackle the above mentioned problems at different levels of the memory hierarchy. The proposed methods utilize techniques like page coloring and shadow addresses and are able to handle a large number of problems ranging from managing wire-delays in large, shared NUCA caches to distributing shared capacity among different cores. We then examine data-placement issues in NUMA main memory for a many-core processor with a moderate number of on-chip memory controllers. Using codesign approaches, we achieve efficient data placement by modifying the operating system's (OS) page allocation algorithm for a wide variety of main memory architectures

Power-constrained aware and latency-aware microarchitectural optimizations in many-core processors

As the transistor budgets outpace the power envelope (the power-wall issue), new architectural and microarchitectural techniques are needed to improve, or at least maintain, the power efficiency of next-generation processors. Run-time adaptation, including core, cache and DVFS adaptations, has recently emerged as a promising area to keep the pace for acceptable power efficiency. However, none of the adaptation techniques proposed so far is able to provide good results when we consider the stringent power budgets that will be common in the next decades, so new techniques that attack the problem from several fronts using different specialized mechanisms are necessary. The combination of different power management mechanisms, however, bring extra levels of complexity, since other factors such as workload behavior and run-time conditions must also be considered to properly allocate power among cores and threads. To address the power issue, this thesis first proposes Chrysso, an integrated and scalable model-driven power management that quickly selects the best combination of adaptation methods out of different core and uncore micro-architecture adaptations, per-core DVFS, or any combination thereof. Chrysso can quickly search the adaptation space by making performance/power projections to identify Pareto-optimal configurations, effectively pruning the search space. Chrysso achieves 1.9x better chip performance over core-level gating for multi-programmed workloads, and 1.5x higher performance for multi-threaded workloads. Most existing power management schemes use a centralized approach to regulate power dissipation. Unfortunately, the complexity and overhead of centralized power management increases significantly with core count rendering it in-viable at fine-grain time slices. The work leverages a two-tier hierarchical power manager. This solution is highly scalable with low overhead on a tiled many-core architecture with shared LLC and per-tile DVFS at fine-grain time slices. The global power is first distributed across tiles using GPM and then within a tile (in parallel across all tiles). Additionally, this work also proposes DVFS and cache-aware thread migration (DCTM) to ensure optimum per-tile co-scheduling of compatible threads at runtime over the two-tier hierarchical power manager. DCTM outperforms existing solutions by up to 12% on adaptive many-core tile processor. With the advancements in the core micro-architectural techniques and technology scaling, the performance gap between the computational component and memory component is increasing significantly (the memory-wall issue). To bridge this gap, the architecture community is pushing forward towards multi-core architecture with on-die near-memory DRAM cache memory (faster than conventional DRAM). Gigascale DRAM Caches poses a problem of how to efficiently manage the tags. The Tags-in-DRAM designs aims at efficiently co-locate tags with data, but it still suffer from high latency especially in multi-way associativity. The thesis finally proposes Tag Cache mechanism, an on-chip distributed tag caching mechanism with limited space and latency overhead to bypass the tag read operation in multi-way DRAM Caches, thereby reducing hit latency. Each Tag Cache, stored in L2, stores tag information of the most recently used DRAM Cache ways. The Tag Cache is able to exploit temporal locality of the DRAM Cache, thereby contributing to on average 46% of the DRAM Cache hits.A mesura que el consum dels transistors supera el nivell de potència desitjable es necessiten noves tècniques arquitectòniques i microarquitectòniques per millorar, o almenys mantenir, l'eficiència energètica dels processadors de les pròximes generacions. L'adaptació en temps d'execució, tant de nuclis com de les cachés, així com també adaptacions DVFS són idees que han sorgit recentment que fan preveure que sigui un àrea prometedora per mantenir un ritme d'eficiència energètica acceptable. Tanmateix, cap de les tècniques d'adaptació proposades fins ara és capaç d'oferir bons resultats si tenim en compte les restriccions estrictes de potència que seran comuns a les pròximes dècades. És convenient definir noves tècniques que ataquin el problema des de diversos fronts utilitzant diferents mecanismes especialitzats. La combinació de diferents mecanismes de gestió d'energia porta aparellada nivells addicionals de complexitat, ja que altres factors com ara el comportament de la càrrega de treball així com condicions específiques de temps d'execució també han de ser considerats per assignar adequadament la potència entre els nuclis del sistema computador. Per tractar el tema de la potència, aquesta tesi proposa en primer lloc Chrysso, una administració d'energia integrada i escalable que selecciona ràpidament la millor combinació entre diferents adaptacions microarquitectòniques. Chrysso pot buscar ràpidament l'adaptació adequada al fer projeccions òptimes de rendiment i potència basades en configuracions de Pareto, permetent així reduir de manera efectiva l'espai de cerca. Chrysso arriba a un rendiment de 1,9 sobre tècniques convencionals d'inhibició de portes amb una càrrega d'aplicacions seqüencials; i un rendiment de 1,5 quan les aplicacions corresponen a programes parla·lels. La majoria dels sistemes de gestió d'energia existents utilitzen un enfocament centralitzat per regular la dissipació d'energia. Malauradament, la complexitat i el temps d'administració s'incrementen significativament amb una gran quantitat de nuclis. En aquest treball es defineix un gestor jeràrquic de potència basat en dos nivells. Aquesta solució és altament escalable amb baix cost operatiu en una arquitectura de múltiples nuclis integrats en clústers, amb memòria caché de darrer nivell compartida a nivell de cluster, i DVFS establert en intervals de temps de gra fi a nivell de clúster. La potència global es distribueix en primer lloc a través dels clústers utilitzant GPM i després es distribueix dins un clúster (en paral·lel si es consideren tots els clústers). A més, aquest treball també proposa DVFS i migració de fils conscient de la memòria caché (DCTM) que garanteix una òptima distribució de tasques entre els nuclis. DCTM supera les solucions existents fins a un 12%. Amb els avenços en la tecnologia i les tècniques de micro-arquitectura de nuclis, la diferència de rendiment entre el component computacional i la memòria està augmentant significativament. Per omplir aquest buit, s'està avançant cap a arquitectures de múltiples nuclis amb memòries caché integrades basades en DRAM. Aquestes memòries caché DRAM a gran escala plantegen el problema de com gestionar de forma eficaç les etiquetes. Els dissenys de cachés amb dades i etiquetes juntes són un primer pas, però encara pateixen per tenir una alta latència, especialment en cachés amb un grau alt d'associativitat. En aquesta tesi es proposa l'estudi d'una tècnica anomenada Tag Cache, un mecanisme distribuït d'emmagatzematge d'etiquetes, que redueix la latència de les operacions de lectura d'etiquetes en les memòries caché DRAM. Cada Tag Cache, que resideix a L2, emmagatzema la informació de les vies que s'han accedit recentment de les memòries caché DRAM. D'aquesta manera es pot aprofitar la localitat temporal d'una caché DRAM, fet que contribueix en promig en un 46% dels encerts en les caché DRAM