Heterogeneous processor pipeline for a product cipher application

Processing data received as a stream is a task commonly performed by modern embedded devices, in a wide range of applications such as multimedia (encoding/decoding/ playing media), networking (switching and routing), digital security, scientific data processing, etc. Such processing normally tends to be calculation intensive and therefore requiring significant processing power. Therefore, hardware acceleration methods to increase the performance of such applications constitute an important area of study. In this paper, we present an evaluation of one such method to process streaming data, namely multi-processor pipeline architecture. The hardware is based on a Multiple-Processor System on Chip (MPSoC), using a data encryption algorithm as a case study. The algorithm is partitioned on a coarse grained level and mapped on to an MPSoC with five processor cores in a pipeline, using specifically configured Xtensa LX3 cores. The system is then selectively optimized by strengthening and pruning the resources of each processor core. The optimized system is evaluated and compared against an optimal single-processor System on Chip (SoC) for the same application. The multiple-processor pipeline system for data encryption algorithms used was observed to provide significant speed ups, up to 4.45 times that of the single-processor system, which is close to the ideal speed up from a five-stage pipeline

Performance Estimation of Task Graphs Based on Path Profiling

Correctly estimating the speed-up of a parallel embedded application is crucial to efficiently compare different parallelization techniques, task graph transformations or mapping and scheduling solutions. Unfortunately, especially in case of control-dominated applications, task correlations may heavily affect the execution time of the solutions and usually this is not properly taken into account during performance analysis. We propose a methodology that combines a single profiling of the initial sequential specification with different decisions in terms of partitioning, mapping, and scheduling in order to better estimate the actual speed-up of these solutions. We validated our approach on a multi-processor simulation platform: experimental results show that our methodology, effectively identifying the correlations among tasks, significantly outperforms existing approaches for speed-up estimation. Indeed, we obtained an absolute error less than 5 % in average, even when compiling the code with different optimization levels

Laitteistokiihdytetyn vuoronnuksen suorituskykyanalyysi

Performance analysis of heterogeneous MPSoCs (Multiprocessor System-on-Chip) is difficult. The non-determinism of parallel computation, communication delays and memory accesses force the system components into complex interaction. Hardware acceleration is used both to speed up the computations and the scheduling on MPSoCs. Finding an accompanying software structuring and efficient scheduling algorithms is not a straightforward task. In this thesis we investigate the use of simulation, measurement and modeling methods for analyzing the performance of heterogeneous MPSoCs. The viewpoint of this thesis is in simulation and modeling: How a high abstraction level simulation methodology can be used in modeling and analyzing of parallel systems based on MPSoCs. In particular we are interested in efficient use of hardware accelerated scheduling mechanisms and how they can be analyzed. Both parallel simulation and simulation of parallel systems contains many different methods, tools and approaches that attempt to balance between competing goals and cope with a specific subset of the problem space. Challenge is that in all approaches most of the simulation and modeling related problems remain and new challenges emerge. This thesis shows that the resource network methodology and dynamic scheduling models are a viable approach in modeling heterogeneous MPSoCs with accelerators. Concrete contributions are based on upgrading an existing simulation framework to support parallelism. Main contribution is on one hand that modeling concepts have been widened, and on the other hand that the supporting mechanisms have been implemented. The thesis work in progress was published in a peer reviewed international scientific workshop and the final results in a peer reviewed international scientific conference. The toolset has also been used in multiuniversity organized teaching and by the industry.Heterogeenisten moniydinjärjestelmien suorituskykyanalyysi on haasteellista. Laskennan epä-deterministisyys, kommunikaatioviiveet ja lukuisat muistioperaatiot saattavat järjestelmän komponentit monimutkaisiin vuorovaikutussuhteisiin. Laitteistokiihdytettyjä ajoitusmenetelmiä käytetään nopeuttamaan ajoituspäätöksiä. Sopivan ohjelmarakenteen ja tehokkaiden ajoitusalgoritmien löytäminen ei ole helppoa. Tässä työssä tutkitaan miten simulointi-, mittaus- ja mallinnusmenetelmiä voi käyttää laitteistokiihdytettyjen moniydinjärjestelmien suorituskykyanalyysiin. Työn näkökulma on simuloinnissa ja mallinnuksessa: Miten korkean abstraktiotason simulointimenetelmät soveltuvat moniydinjärjestelmiin pohjautuvien rinnakkaisten järjestelmien mallinnukseen ja suorituskykyanalyysiin. Erityisen kiinnostuksen kohteena on laitteistokiihdytteisten ajoitusmenetelmien tehokas käyttö sekä analysointi. Rinnakkaissimulointi pitää sisällään erilaisia menetelmiä, työkaluja ja lähestymistapoja jotka pyrkivät tasapainottelemaan ristiriitaisten tavoitteiden välillä. Haasteena on se, että kaikissa lähestymistavoissa simulaation ja mallinnuksen useimmat ongelmat säilyvät ja uusia ongelmia ilmaantuu. Työn tulokset viittaavat siihen että resurssiverkkopohjainen menetelmä dynaamisen ajoituksen kanssa on toimiva lähestymistapa rinnakkaisten järjestelmien suorituskykyanalyysiin. Työn konkreettiset tulokset pitävät sisällään olemassa olevan simulointiympäristön päivittämisen rinnakkaisuutta tukevaksi. Keskeinen tulos on toisaalta se että mallinnusmenetelmiä on laajennettu ja toisaalta se että näitä tukevat mekanismit on toteutettu. Keskeneräisen työn tulokset on julkaistu vertaisarvioidussa tieteellisessä seminaarissa ja valmiin työn tulokset vertaisarvioidussa tieteellisessä konferenssissa. Simulointiympäristöä on käytetty usean yliopiston järjestämässä yhteisopetuksessa sekä teollisuudessa

Automatic parallelization for embedded multi-core systems using high level cost models

Nowadays, embedded and cyber-physical systems are utilized in nearly all operational areas in order to support and enrich peoples' everyday life. To cope with the demands imposed by modern embedded systems, the employment of MPSoC devices is often the most profitable solution. However, many embedded applications are still written in a sequential way. In order to benefit from the multiple cores available on those devices, the application code has to be divided into concurrently executed tasks. Since performing this partitioning manually is an error-prone and also time-consuming job, many automatic parallelization approaches were developed in the past. Most of these existing approaches were developed in the context of high-performance and desktop computers so that their applicability to embedded devices is limited. Many new challenges arise if applications should be ported to embedded MPSoCs in an efficient way. Therefore, novel parallelization techniques were developed in the context of this thesis that are tailored towards special requirements demanded by embedded multi-core devices. All approaches presented in this thesis are based on sophisticated parallelization techniques employing high-level cost models to estimate the benefit of parallel execution. This enables the creation of well-balanced tasks, which is essential if applications should be parallelized efficiently. In addition, several other requirements of embedded devices are covered, like the consideration of multiple objectives simultaneously. As a result, beneficial trade-offs between several objectives, like, e.g., energy consumption and execution time can be found enabling the extraction of solutions which are highly optimized for a specific application scenario. To be applicable to many embedded application domains, approaches extracting different kinds of parallelism were also developed. The structure of the global parallelization approach facilitates the combination of different approaches in a plug-and-play fashion. Thus, the advantages of multiple parallelization techniques can easily be combined. Finally, in addition to parallelization approaches for homogeneous MPSoCs, optimized ones for heterogeneous devices were also developed in this thesis since the trend towards heterogeneous multi-core architectures is inexorable. To the best of the author's knowledge, most of these objectives and especially their combination were not covered by existing parallelization frameworks, so far. By combining all of them, a parallelization framework that is well optimized for embedded multi-core devices was developed in the context of this thesis

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Performance Modeling of Parallel Applications on MPSoCs

In this paper we present a new technique for automatically measuring the performance of tasks, functions or arbitrary parts of a program on a multiprocessor embedded system. The technique instruments the tasks described by OpenMP, used to represent the task parallelism, while ad hoc pragmas in the source indicate other pieces of code to profile. The annotations and the instrumentation are completely target-independent, so the same code can be measured on different target architectures, on simulators or on prototypes. We validate the approach on a single and on a dual LEON 3 platform synthesized on FPGA, demonstrating a low instrumentation overhead. We show how the information obtained with this technique can be easily exploited in a hardware/software design space exploration tool, by estimating, with good accuracy, the speed-up of a parallel application given the profiling on the single processor prototype

Piattaforme multicore e integrazione tri-dimensionale: analisi architetturale e ottimizzazione

Modern embedded systems embrace many-core shared-memory designs. Due to constrained power and area budgets, most of them feature software-managed scratchpad memories instead of data caches to increase the data locality. It is therefore programmers’ responsibility to explicitly manage the memory transfers, and this make programming these platform cumbersome. Moreover, complex modern applications must be adequately parallelized before they can the parallel potential of the platform into actual performance. To support this, programming languages were proposed, which work at a high level of abstraction, and rely on a runtime whose cost hinders performance, especially in embedded systems, where resources and power budget are constrained. This dissertation explores the applicability of the shared-memory paradigm on modern many-core systems, focusing on the ease-of-programming. It focuses on OpenMP, the de-facto standard for shared memory programming. In a first part, the cost of algorithms for synchronization and data partitioning are analyzed, and they are adapted to modern embedded many-cores. Then, the original design of an OpenMP runtime library is presented, which supports complex forms of parallelism such as multi-level and irregular parallelism. In the second part of the thesis, the focus is on heterogeneous systems, where hardware accelerators are coupled to (many-)cores to implement key functional kernels with orders-of-magnitude of speedup and energy efficiency compared to the “pure software” version. However, three main issues rise, namely i) platform design complexity, ii) architectural scalability and iii) programmability. To tackle them, a template for a generic hardware processing unit (HWPU) is proposed, which share the memory banks with cores, and the template for a scalable architecture is shown, which integrates them through the shared-memory system. Then, a full software stack and toolchain are developed to support platform design and to let programmers exploiting the accelerators of the platform. The OpenMP frontend is extended to interact with it.I sistemi integrati moderni sono architetture many-core, in cui spesso lo spazio di memoria è condiviso fra i processori. Per ridurre i consumi, molte di queste architetture sostituiscono le cache dati con memorie scratchpad gestite in software, per massimizzarne la località alle CPU e aumentare le performance. Questo significa che i dati devono essere spostati manualmente da parte del programmatore. Inoltre, tradurre in perfomance l’enorme parallelismo potenziale delle piattaforme many-core non è semplice. Per supportare la programmazione, diversi programming model sono stati proposti, e siccome lavorano ad un alto livello di astrazione, sfruttano delle librerie di runtime che forniscono servizi di base quali sincronizzazione, allocazione della memoria, threading. Queste librerie hanno un costo, che nei sistemi integrati è troppo elevato e ostacola il raggiungimento delle piene performance. Questa tesi analizza come un programming model ad alto livello di astrazione – OpenMP – possa essere efficientemente supportato, se il suo stack software viene adattato per sfruttare al meglio la piattaforma sottostante. In una prima parte, studio diversi meccanismi di sincronizzazione e comunicazione fra thread paralleli, portati sulle piattaforme many-core. In seguito, li utilizzo per scrivere un runtime di supporto a OpenMP che sia il più possibile efficente e “leggero” e che supporti paradigmi di parallelismo multi-livello e irregolare, spesso presenti nelle applicazioni moderne. Una seconda parte della tesi esplora le architetture eterogenee, ossia con acceleratori hardware. Queste architetture soffrono di problematiche sia i) per il processo di design della piattaforma, che ii) di scalabilità della piattaforma stessa (aumento del numero degli acceleratori e dei processori), che iii) di programmabilità. La tesi propone delle soluzioni a tutti e tre i problemi. Il linguaggio di programmazione usato è OpenMP, sia per la sua grande espressività a livello semantico, sia perché è lo standard de-facto per programmare sistemi a memoria condivisa