Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems

Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling applications in heterogeneous machines remains challenging. Additionally, when these heterogeneous resources accommodate multiple applications to increase utilization, resources are prone to contention, destructive interference, and unpredictable performance. Existing solutions examine heterogeneity either across or within a server, leading to missed performance and efficiency opportunities. We present Mage, a practical interference-aware runtime that optimizes performance and efficiency in systems with intra- and inter-server heterogeneity. Mage leverages fast and online data mining to quickly explore the space of application placements, and determine the one that minimizes destructive interference between co-resident applications. Mage continuously monitors the performance of active applications, and, upon detecting QoS violations, it determines whether alternative placements would prove more beneficial, taking into account any overheads from migration. Across 350 application mixes on a heterogeneous CMP, Mage improves performance by 38% and up to 2x compared to a greedy scheduler. Across 160 mixes on a heterogeneous cluster, Mage improves performance by 30% on average and up to 52% over the greedy scheduler, and by 11% over the combination of Paragon [15] for inter- and intra-server heterogeneity

Improving cache Behavior in CMP architectures throug cache partitioning techniques

The evolution of microprocessor design in the last few decades has changed significantly, moving from simple inorder single core architectures to superscalar and vector architectures in order to extract the maximum available instruction level parallelism. Executing several instructions from the same thread in parallel allows significantly improving the performance of an application. However, there is only a limited amount of parallelism available in each thread, because of data and control dependences. Furthermore, designing a high performance, single, monolithic processor has become very complex due to power and chip latencies constraints. These limitations have motivated the use of thread level parallelism (TLP) as a common strategy for improving processor performance. Multithreaded processors allow executing different threads at the same time, sharing some hardware resources. There are several flavors of multithreaded processors that exploit the TLP, such as chip multiprocessors (CMP), coarse grain multithreading, fine grain multithreading, simultaneous multithreading (SMT), and combinations of them.To improve cost and power efficiency, the computer industry has adopted multicore chips. In particular, CMP architectures have become the most common design decision (combined sometimes with multithreaded cores). Firstly, CMPs reduce design costs and average power consumption by promoting design re-use and simpler processor cores. For example, it is less complex to design a chip with many small, simple cores than a chip with fewer, larger, monolithic cores.Furthermore, simpler cores have less power hungry centralized hardware structures. Secondly, CMPs reduce costs by improving hardware resource utilization. On a multicore chip, co-scheduled threads can share costly microarchitecture resources that would otherwise be underutilized. Higher resource utilization improves aggregate performance and enables lower cost design alternatives.One of the resources that impacts most on the final performance of an application is the cache hierarchy. Caches store data recently used by the applications in order to take advantage of temporal and spatial locality of applications. Caches provide fast access to data, improving the performance of applications. Caches with low latencies have to be small, which prompts the design of a cache hierarchy organized into several levels of cache.In CMPs, the cache hierarchy is normally organized in a first level (L1) of instruction and data caches private to each core. A last level of cache (LLC) is normally shared among different cores in the processor (L2, L3 or both). Shared caches increase resource utilization and system performance. Large caches improve performance and efficiency by increasing the probability that each application can access data from a closer level of the cache hierarchy. It also allows an application to make use of the entire cache if needed.A second advantage of having a shared cache in a CMP design has to do with the cache coherency. In parallel applications, different threads share the same data and keep a local copy of this data in their cache. With multiple processors, it is possible for one processor to change the data, leaving another processor's cache with outdated data. Cache coherency protocol monitors changes to data and ensures that all processor caches have the most recent data. When the parallel application executes on the same physical chip, the cache coherency circuitry can operate at the speed of on-chip communications, rather than having to use the much slower between-chip communication, as is required with discrete processors on separate chips. These coherence protocols are simpler to design with a unified and shared level of cache onchip.Due to the advantages that multicore architectures offer, chip vendors use CMP architectures in current high performance, network, real-time and embedded systems. Several of these commercial processors have a level of the cache hierarchy shared by different cores. For example, the Sun UltraSPARC T2 has a 16-way 4MB L2 cache shared by 8 cores each one up to 8-way SMT. Other processors like the Intel Core 2 family also share up to a 12MB 24-way L2 cache. In contrast, the AMD K10 family has a private L2 cache per core and a shared L3 cache, with up to a 6MB 64-way L3 cache.As the long-term trend of increasing integration continues, the number of cores per chip is also projected to increase with each successive technology generation. Some significant studies have shown that processors with hundreds of cores per chip will appear in the market in the following years. The manycore era has already begun. Although this era provides many opportunities, it also presents many challenges. In particular, higher hardware resource sharing among concurrently executing threads can cause individual thread's performance to become unpredictable and might lead to violations of the individual applications' performance requirements. Current resource management mechanisms and policies are no longer adequate for future multicore systems.Some applications present low re-use of their data and pollute caches with data streams, such as multimedia, communications or streaming applications, or have many compulsory misses that cannot be solved by assigning more cache space to the application. Traditional eviction policies such as Least Recently Used (LRU), pseudo LRU or random are demand-driven, that is, they tend to give more space to the application that has more accesses to the cache hierarchy.When no direct control over shared resources is exercised (the last level cache in this case), it is possible that a particular thread allocates most of the shared resources, degrading other threads performance. As a consequence, high resource sharing and resource utilization can cause systems to become unstable and violate individual applications' requirements. If we want to provide a Quality of Service (QoS) to applications, we need to enhance the control over shared resources and enrich the collaboration between the OS and the architecture.In this thesis, we propose software and hardware mechanisms to improve cache sharing in CMP architectures. We make use of a holistic approach, coordinating targets of software and hardware to improve system aggregate performance and provide QoS to applications. We make use of explicit resource allocation techniques to control the shared cache in a CMP architecture, with resource allocation targets driven by hardware and software mechanisms.The main contributions of this thesis are the following:- We have characterized different single- and multithreaded applications and classified workloads with a systematic method to better understand and explain the cache sharing effects on a CMP architecture. We have made a special effort in studying previous cache partitioning techniques for CMP architectures, in order to acquire the insight to propose improved mechanisms.- In CMP architectures with out-of-order processors, cache misses can be served in parallel and share the miss penalty to access main memory. We take this fact into account to propose new cache partitioning algorithms guided by the memory-level parallelism (MLP) of each application. With these algorithms, the system performance is improved (in terms of throughput and fairness) without significantly increasing the hardware required by previous proposals.- Driving cache partition decisions with indirect indicators of performance such as misses, MLP or data re-use may lead to suboptimal cache partitions. Ideally, the appropriate metric to drive cache partitions should be the target metric to optimize, which is normally related to IPC. Thus, we have developed a hardware mechanism, OPACU, which is able to obtain at run-time accurate predictions of the performance of an application when running with different cache assignments.- Using performance predictions, we have introduced a new framework to manage shared caches in CMP architectures, FlexDCP, which allows the OS to optimize different IPC-related target metrics like throughput or fairness and provide QoS to applications. FlexDCP allows an enhanced coordination between the hardware and the software layers, which leads to improved system performance and flexibility.- Next, we have made use of performance estimations to reduce the load imbalance problem in parallel applications. We have built a run-time mechanism that detects parallel applications sensitive to cache allocation and, in these situations, the load imbalance is reduced by assigning more cache space to the slowest threads. This mechanism, helps reducing the long optimization time in terms of man-years of effort devoted to large-scale parallel applications.- Finally, we have stated the main characteristics that future multicore processors with thousands of cores should have. An enhanced coordination between the software and hardware layers has been proposed to better manage the shared resources in these architectures

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Towards Successful Application of Phase Change Memories: Addressing Challenges from Write Operations

The emerging Phase Change Memory (PCM) technology is drawing increasing attention due to its advantages in non-volatility, byte-addressability and scalability. It is regarded as a promising candidate for future main memory. However, PCM's write operation has some limitations that pose challenges to its application in memory. The disadvantages include long write latency, high write power and limited write endurance. In this thesis, I present my effort towards successful application of PCM memory. My research consists of several optimizing techniques at both the circuit and architecture level. First, at the circuit level, I propose Differential Write to remove unnecessary bit changes in PCM writes. This is not only beneficial to endurance but also to the energy and latency of writes. Second, I propose two memory scheduling enhancements (AWP and RAWP) for a non-blocking bank design. My memory scheduling enhancements can exploit intra-bank parallelism provided by non-blocking bank design, and achieve significant throughput improvement. Third, I propose Bit Level Power Budgeting (BPB), a fine-grained power budgeting technique that leverages the information from Differential Write to achieve even higher memory throughput under the same power budget. Fourth, I propose techniques to improve the QoS tuning ability of high-priority applications when running on PCM memory. In summary, the techniques I propose effectively address the challenges of PCM's write operations. In addition, I present the experimental infrastructure in this work and my visions of potential future research topics, which could be helpful to other researchers in the area

Predicting Application Performance for Chip Multiprocessors

Today's computers have processors with multiple cores that allow several applications to execute simultaneously. The way resources are allocated to an application affects whether performance objectives, such as quality of service (QoS), are satisfied. To ensure objectives are met, resources must be carefully but quickly allocated in response to changing runtime conditions. Traditional approaches to resource allocation take place either purely online or offline. Online methods do not scale to large, multiple core systems because there are too many allocations to evaluate at runtime. Offline methods cannot handle unanticipated workloads or changes. A hybrid approach could combine the lower runtime overhead of offline approaches with the flexibility of online approaches. This thesis introduces AUTO, a hybrid solution to perform resource allocation. AUTO dynamically adjusts thread count, core count, and core type. It does so in accordance with a user-provided policy to meet performance objectives. AUTO's capabilities come from four prediction techniques. The first technique builds and uses models that consider CPU contention and application scalability in order to select co-running applications' thread counts. The second technique predicts applications' preferred thread-to-core mappings. The predictions are thread count independent and are translated into concrete thread-to-core mappings based on resource availability. The third technique predicts application performance under thread-to-core mappings. The final technique selects thread count and core count for applications on a system with cores of different capabilities. AUTO was tested in several scenarios. In each scenario, it was shown to be an effective, efficient solution to resource allocation. First, it was used to select the thread count of one or more co-running applications. Second, it was used to select application thread-to-core mappings. Third, it was used to make predictions about application performance under thread-to-core mappings. Finally, it was used to select both thread count and core type for applications on a computer with cores of different capabilities. AUTO's resource allocation and models allow for more effective and more efficient policies. By using hybrid online and offline techniques, AUTO solves the problem of allocating threads and cores to meet performance objectives

Software-based and regionally-oriented traffic management in Networks-on-Chip

Since the introduction of chip-multiprocessor systems, the number of integrated cores has been steady growing and workload applications have been adapted to exploit the increasing parallelism. This changed the importance of efficient on-chip communication significantly and the infrastructure has to keep step with these new requirements. The work at hand makes significant contributions to the state-of-the-art of the latest generation of such solutions, called Networks-on-Chip, to improve the performance, reliability, and flexible management of these on-chip infrastructures

Memory region: a system abstraction for managing the complex memory structures of multicore platforms

The performance of modern many-core systems depends on the effective use of their complex cache and memory structures, and this will likely become more pronounced with the impending arrival of on-chip 3D stacked and non-volatile off-chip byte-addressable memory. Yet to date, operating systems have not treated memory as a first class schedulable resource, embracing memory heterogeneity. This dissertation presents a new software abstraction, called ‘memory region’, which denotes the current set of physical memory pages actively used by workloads. Using this abstraction, memory resources can be scheduled for applications to fully exploit a platform's underlying cache and memory system, thereby gaining improved performance and predictability in execution, particularly for the consolidated workloads seen in virtualized and cloud computing infrastructures. The abstraction's implementation in the Xen hypervisor involves the run-time detection of memory regions, the scheduled mapping of these regions to caches to match performance goals, and maintaining region-to-cache mappings using per-cache page tables. This dissertation makes the following specific contributions. First, its region scheduling method proposes that the location of memory blocks rather than CPU utilization is the principal determinant where workloads are run. It proposes a new scheduling method, the region scheduling that the location of memory blocks determines where the workloads are run. Second, treating memory blocks as first-class resources, new methods for efficient cache management are shown to improve application performance as well as the performance of certain operating system functions. Third, explicit memory scheduling makes it possible to disaggregate operating systems, without the need to change OS sources and with only small markups of target guest OS functionality. With this method, OS functions can be mapped to specific desired platform components, such as file system confined to running on specific cores and using only certain memory resources designated for its use. This can improve performance for applications heavily dependent on certain OS functions, by dynamically providing those functions with the resources needed for their current use, and it can prevent performance-critical application functionality from being needlessly perturbed by OS functions used for other purposes or by other jobs. Fourth, extensions of region scheduling can also help applications deal with the heterogeneous memory resources present in future systems, including on-chip stacked DRAM and NUMA or even NVRAM memory modules. More generally, regions scheduling is shown to apply to memory structures with well-defined differences in memory access latencies.Ph.D

Adaptive memory hierarchies for next generation tiled microarchitectures

Les últimes dècades el rendiment dels processadors i de les memòries ha millorat a diferent ritme, limitant el rendiment dels processadors i creant el conegut memory gap. Sol·lucionar aquesta diferència de rendiment és un camp d'investigació d'actualitat i que requereix de noves sol·lucions. Una sol·lució a aquest problema són les memòries “cache”, que permeten reduïr l'impacte d'unes latències de memòria creixents i que conformen la jerarquia de memòria. La majoria de d'organitzacions de les “caches” estan dissenyades per a uniprocessadors o multiprcessadors tradicionals. Avui en dia, però, el creixent nombre de transistors disponible per xip ha permès l'aparició de xips multiprocessador (CMPs). Aquests xips tenen diferents propietats i limitacions i per tant requereixen de jerarquies de memòria específiques per tal de gestionar eficientment els recursos disponibles. En aquesta tesi ens hem centrat en millorar el rendiment i la eficiència energètica de la jerarquia de memòria per CMPs, des de les “caches” fins als controladors de memòria. A la primera part d'aquesta tesi, s'han estudiat organitzacions tradicionals per les “caches” com les privades o compartides i s'ha pogut constatar que, tot i que funcionen bé per a algunes aplicacions, un sistema que s'ajustés dinàmicament seria més eficient. Tècniques com el Cooperative Caching (CC) combinen els avantatges de les dues tècniques però requereixen un mecanisme centralitzat de coherència que té un consum energètic molt elevat. És per això que en aquesta tesi es proposa el Distributed Cooperative Caching (DCC), un mecanisme que proporciona coherència en CMPs i aplica el concepte del cooperative caching de forma distribuïda. Mitjançant l'ús de directoris distribuïts s'obté una sol·lució més escalable i que, a més, disposa d'un mecanisme de marcatge més flexible i eficient energèticament. A la segona part, es demostra que les aplicacions fan diferents usos de la “cache” i que si es realitza una distribució de recursos eficient es poden aprofitar els que estan infrautilitzats. Es proposa l'Elastic Cooperative Caching (ElasticCC), una organització capaç de redistribuïr la memòria “cache” dinàmicament segons els requeriments de cada aplicació. Una de les contribucions més importants d'aquesta tècnica és que la reconfiguració es decideix completament a través del maquinari i que tots els mecanismes utilitzats es basen en estructures distribuïdes, permetent una millor escalabilitat. ElasticCC no només és capaç de reparticionar les “caches” segons els requeriments de cada aplicació, sinó que, a més a més, és capaç d'adaptar-se a les diferents fases d'execució de cada una d'elles. La nostra avaluació també demostra que la reconfiguració dinàmica de l'ElasticCC és tant eficient que gairebé proporciona la mateixa taxa de fallades que una configuració amb el doble de memòria.Finalment, la tesi es centra en l'estudi del comportament de les memòries DRAM i els seus controladors en els CMPs. Es demostra que, tot i que els controladors tradicionals funcionen eficientment per uniprocessadors, en CMPs els diferents patrons d'accés obliguen a repensar com estan dissenyats aquests sistemes. S'han presentat múltiples sol·lucions per CMPs però totes elles es veuen limitades per un compromís entre el rendiment global i l'equitat en l'assignació de recursos. En aquesta tesi es proposen els Thread Row Buffers (TRBs), una zona d'emmagatenament extra a les memòries DRAM que permetria guardar files de dades específiques per a cada aplicació. Aquest mecanisme permet proporcionar un accés equitatiu a la memòria sense perjudicar el seu rendiment global. En resum, en aquesta tesi es presenten noves organitzacions per la jerarquia de memòria dels CMPs centrades en la escalabilitat i adaptativitat als requeriments de les aplicacions. Els resultats presentats demostren que les tècniques proposades proporcionen un millor rendiment i eficiència energètica que les millors tècniques existents fins a l'actualitat.Processor performance and memory performance have improved at different rates during the last decades, limiting processor performance and creating the well known "memory gap". Solving this performance difference is an important research field and new solutions must be proposed in order to have better processors in the future. Several solutions exist, such as caches, that reduce the impact of longer memory accesses and conform the system memory hierarchy. However, most of the existing memory hierarchy organizations were designed for single processors or traditional multiprocessors. Nowadays, the increasing number of available transistors has allowed the apparition of chip multiprocessors, which have different constraints and require new ad-hoc memory systems able to efficiently manage memory resources. Therefore, in this thesis we have focused on improving the performance and energy efficiency of the memory hierarchy of chip multiprocessors, ranging from caches to DRAM memories. In the first part of this thesis we have studied traditional cache organizations such as shared or private caches and we have seen that they behave well only for some applications and that an adaptive system would be desirable. State-of-the-art techniques such as Cooperative Caching (CC) take advantage of the benefits of both worlds. This technique, however, requires the usage of a centralized coherence structure and has a high energy consumption. Therefore we propose the Distributed Cooperative Caching (DCC), a mechanism to provide coherence to chip multiprocessors and apply the concept of cooperative caching in a distributed way. Through the usage of distributed directories we obtain a more scalable solution and, in addition, has a more flexible and energy-efficient tag allocation method. We also show that applications make different uses of cache and that an efficient allocation can take advantage of unused resources. We propose Elastic Cooperative Caching (ElasticCC), an adaptive cache organization able to redistribute cache resources dynamically depending on application requirements. One of the most important contributions of this technique is that adaptivity is fully managed by hardware and that all repartitioning mechanisms are based on distributed structures, allowing a better scalability. ElasticCC not only is able to repartition cache sizes to application requirements, but also is able to dynamically adapt to the different execution phases of each thread. Our experimental evaluation also has shown that the cache partitioning provided by ElasticCC is efficient and is almost able to match the off-chip miss rate of a configuration that doubles the cache space. Finally, we focus in the behavior of DRAM memories and memory controllers in chip multiprocessors. Although traditional memory schedulers work well for uniprocessors, we show that new access patterns advocate for a redesign of some parts of DRAM memories. Several organizations exist for multiprocessor DRAM schedulers, however, all of them must trade-off between memory throughput and fairness. We propose Thread Row Buffers, an extended storage area in DRAM memories able to store a data row for each thread. This mechanism enables a fair memory access scheduling without hurting memory throughput. Overall, in this thesis we present new organizations for the memory hierarchy of chip multiprocessors which focus on the scalability and of the proposed structures and adaptivity to application behavior. Results show that the presented techniques provide a better performance and energy-efficiency than existing state-of-the-art solutions