Technical Report: Benchmarking an HP DL580 cluster at Indiana University (Mason)

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies that supported this work.Detailed system description and benchmark performance of Mason, an HP DL580 system installed in 2011

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

Measurement, Modeling, and Characterization for Energy-Efficient Computing

The ever-increasing ecological footprint of Information Technology (IT) sector coupled with adverse effects of high power consumption on electronic circuits has increased the significance of energy-efficient computing in the last decade. Making energy-efficient computing a norm rather than an exception requires that system designers and programmers understand the energy implications of their design and implementation choices. This necessitates a detailed view of system’s energy expenditure and/or power consumption. We explore this aspect of energy-efficient computing in this thesis through power measurement, power modeling, and energy characterization.First, we present a quantitative comparison between power measurement data collected for computer systems using four techniques: a power meter at wall outlet, currenttransducers at ATX power rails, CPU voltage regulator’s current monitor, and Intel’s proprietary RAPL (Running Average Power Limit) interface. We compare them for accuracy, sensitivity and accessibility.Second, we present two different methodologies to model processor power consumption. The first model estimates power consumption at the granularity of individualcores using per-core performance events and temperature sensors. We validate the methodology on six different platforms and show that our model estimates power consumption with high accuracy across all platforms consistently. To understand the energy expenditure trends across different frequencies and different degrees of parallelism, we need to model power at a much finer granularity. The second power model addresses this issue by estimating static and dynamic power consumption for individual cores and the uncore. We validate this model on Intel’s Haswell platform for single-threaded and multi-threaded benchmarks. We use this power model to characterize energy efficiency of frequency scaling on Haswell microarchitecture and use the insights to implementa low overhead DVFS scheduler. We also characterize the energy efficiency of thread scaling using the power model and demonstrate how different communication parametersand microarchitectural traits affect application’s energy when it scales.Finally, we perform detailed performance and energy characterization of Intel’s RestrictedTransactional Memory (RTM).We use TinySTM software transactional memory(STM) system to benchmark RTM’s performance against competing STM alternatives.We use microbenchmarks and STAMP benchmark suite to compare RTM an STM performanceand energy behavior. We quantify the RTM hardware limitations and identifyconditions required for RTM to outperform STM

Operating system support for overlapping-ISA heterogeneous multi-core architectures

A heterogeneous processor consists of cores that are asymmetric in performance and functionality. Such a de-sign provides a cost-effective solution for processor man-ufacturers to continuously improve both single-thread per-formance and multi-thread throughput. This design, how-ever, faces significant challenges in the operating system, which traditionally assumes only homogeneous hardware. This paper presents a comprehensive study of OS support for heterogeneous architectures in which cores have asym-metric performance and overlapping, but non-identical in-struction sets. Our algorithms allow applications to trans-parently execute and fairly share different types of cores. We have implemented these algorithms in the Linux 2.6.24 kernel and evaluated them on an actual heterogeneous plat-form. Evaluation results demonstrate that our designs effi-ciently manage heterogeneous hardware and enable signifi-cant performance improvements for a range of applications.

PARSEC vs. SPLASH-2: A quantitative comparison of two multithreaded benchmark suites on Chip-Multiprocessors

The PARSEC benchmark suite was recently released and has been adopted by a significant number of users within a short amount of time. This new collection of workloads is not yet fully under-stood by researchers. In this study we compare the SPLASH-2 and PARSEC benchmark suites with each other to gain insights into differences and similarities between the two program collections. We use standard statistical methods and machine learning to ana-lyze the suites for redundancy and overlap on Chip-Multiprocessors (CMPs). Our analysis shows that PARSEC workloads are funda-mentally different from SPLASH-2 benchmarks. The observed dif-ferences can be explained with two technology trends, the prolifer-ation of CMPs and the accelerating growth of world data

Portable, scalable, per-core power estimation for intelligent resource management

Performance, power, and temperature are now all first-order design constraints. Balancing power efficiency, thermal constraints, and performance requires some means to convey data about real-time power consumption and temperature to intelligent resource managers. Resource managers can use this information to meet performance goals, maintain power budgets, and obey thermal constraints. Unfortunately, obtaining the required machine introspection is challenging. Most current chips provide no support for per-core power monitoring, and when support exists, it is not exposed to software. We present a methodology for deriving per-core power models using sampled performance counter values and temperature sensor readings. We develop application-independent models for four different (four- to eight-core) platforms, validate their accuracy, and show how they can be used to guide scheduling decisions in power-aware resource managers. Model overhead is negligible, and estimations exhibit 1.1%-5.2% per-suite median error on the NAS, SPEC OMP, and SPEC 2006 benchmarks (and 1.2%-4.4% overall)

Multicore-Aware Reuse Distance Analysis

This paper presents and validates methods to extend reuse distance analysis of application locality characteristics to shared-memory multicore platforms by accounting for invalidation-based cache-coherence and inter-core cache sharing. Existing reuse distance analysis methods track the number of distinct addresses referenced between reuses of the same address by a given thread, but do not model the effects of data references by other threads. This paper shows several methods to keep reuse stacks consistent so that they account for invalidations and cache sharing, either as references arise in a simulated execution or at synchronization points. These methods are evaluated against a Simics-based coherent cache simulator running several OpenMP and transaction-based benchmarks. The results show that adding multicore-awareness substantially improves the ability of reuse distance analysis to model cache behavior, reducing the error in miss ratio prediction (relative to cache simulation for a specific cache size) by an average of 69% for per-core caches and an average of 84% for shared caches

A compiler level intermediate representation based binary analysis system and its applications

Analyzing and optimizing programs from their executables has received a lot of attention recently in the research community. There has been a tremendous amount of activity in executable-level research targeting varied applications such as security vulnerability analysis, untrusted code analysis, malware analysis, program testing, and binary optimizations. The vision of this dissertation is to advance the field of static analysis of executables and bridge the gap between source-level analysis and executable analysis. The main thesis of this work is scalable static binary rewriting and analysis using compiler-level intermediate representation without relying on the presence of metadata information such as debug or symbolic information. In spite of a significant overlap in the overall goals of several source-code methods and executables-level techniques, several sophisticated transformations that are well-understood and implemented in source-level infrastructures have yet to become available in executable frameworks. It is a well known fact that a standalone executable without any meta data is less amenable to analysis than the source code. Nonetheless, we believe that one of the prime reasons behind the limitations of existing executable frameworks is that current executable frameworks define their own intermediate representations (IR) which are significantly more constrained than an IR used in a compiler. Intermediate representations used in existing binary frameworks lack high level features like abstract stack, variables, and symbols and are even machine dependent in some cases. This severely limits the application of well-understood compiler transformations to executables and necessitates new research to make them applicable. In the first part of this dissertation, we present techniques to convert the binaries to the same high-level intermediate representation that compilers use. We propose methods to segment the flat address space in an executable containing undifferentiated blocks of memory. We demonstrate the inadequacy of existing variable identification methods for their promotion to symbols and present our methods for symbol promotion. We also present methods to convert the physically addressed stack in an executable to an abstract stack. The proposed methods are practical since they do not employ symbolic, relocation, or debug information which are usually absent in deployed executables. We have integrated our techniques with a prototype x86 binary framework called \emph{SecondWrite} that uses LLVM as the IR. The robustness of the framework is demonstrated by handling executables totaling more than a million lines of source-code, including several real world programs. In the next part of this work, we demonstrate that several well-known source-level analysis frameworks such as symbolic analysis have limited effectiveness in the executable domain since executables typically lack higher-level semantics such as program variables. The IR should have a precise memory abstraction for an analysis to effectively reason about memory operations. Our first work of recovering a compiler-level representation addresses this limitation by recovering several higher-level semantics information from executables. In the next part of this work, we propose methods to handle the scenarios when such semantics cannot be recovered. First, we propose a hybrid static-dynamic mechanism for recovering a precise and correct memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise memory model and ignore memory-allocated variables while defining symbolic analysis mechanisms. We exemplify that our framework is robust, efficient and it significantly improves the performance of various traditional analyses like global value numbering, alias analysis and dependence analysis for executables. Finally, the underlying representation and analysis framework is employed for two separate applications. First, the framework is extended to define a novel static analysis framework, \emph{DemandFlow}, for identifying information flow security violations in program executables. Unlike existing static vulnerability detection methods for executables, DemandFlow analyzes memory locations in addition to symbols, thus improving the precision of the analysis. DemandFlow proposes a novel demand-driven mechanism to identify and precisely analyze only those program locations and memory accesses which are relevant to a vulnerability, thus enhancing scalability. DemandFlow uncovers six previously undiscovered format string and directory traversal vulnerabilities in popular ftp and internet relay chat clients. Next, the framework is extended to implement a platform-specific optimization for embedded processors. Several embedded systems provide the facility of locking one or more lines in the cache. We devise the first method in literature that employs instruction cache locking as a mechanism for improving the average-case run-time of general embedded applications. We demonstrate that the optimal solution for instruction cache locking can be obtained in polynomial time. Since our scheme is implemented inside a binary framework, it successfully addresses the portability concern by enabling the implementation of cache locking at the time of deployment when all the details of the memory hierarchy are available

Measurement, Modeling, and Characterization for Power-Aware Computing

Society’s increasing dependence on information technology has resulted in the deployment of vast compute resources. The energy costs of operating these resources coupled with environmental concerns have made power-aware computingone of the primary challenges for the IT sector. Making energy-efficient computing a rule rather than an exception requires that researchers and system designers use the right set of techniques and tools. These involve measuring,modeling, and characterizing the energy consumption of computers at varying degrees of granularity.In this thesis, we present techniques to measure power consumption of computer systems at various levels. We compare them for accuracy and sensitivityand discuss their effectiveness. We test Intel’s hardware power model for estimation accuracy and show that it is fairly accurate for estimating energy consumption when sampled at the temporal granularity of more than tens ofmilliseconds.We present a methodology to estimate per-core processor power consumption using performance counter and temperature-based power modeling and validate it across multiple platforms. We show our model exhibits negligible computationoverhead, and the median estimation errors ranges from 0.3% to 10.1% for applications from SPEC2006, SPEC-OMP and NAS benchmarks. We test the usefulness of the model in a meta-scheduler to enforce power constraint on a system.Finally, we perform a detailed performance and energy characterization of Intel’s Restricted Transactional Memory (RTM). We use TinySTM software transactional memory (STM) system to benchmark RTM’s performance against competing STM alternatives. We use microbenchmarks and STAMP benchmarksuite to compare RTM versus STM performance and energy behavior. We quantify the RTM hardware limitations that affect its success rate. We show that RTM performs better than TinySTM when working-set fits inside the cache and that RTM is better at handling high contention workloads