Alignment of Memory Transfers of a Time-Predictable Stack Cache

N/AModern computer architectures use features which often com-plicate the WCET analysis of real-time software. Alterna-tive time-predictable designs, and in particular caches, thus are gaining more and more interest. A recently proposed stack cache, for instance, avoids the need for the analysis of complex cache states. Instead, only the occupancy level of the cache has to be determined. The memory transfers generated by the standard stack cache are not generally aligned. These unaligned accesses risk to introduce complexity to the otherwise simple WCET analysis. In this work, we investigate three different ap-proaches to handle the alignment problem in the stack cache: (1) unaligned transfers, (2) alignment through compiler-gen-erated padding, (3) a novel hardware extension ensuring the alignment of all transfers. Simulation results show that our hardware extension offers a good compromise between average-case performance and analysis complexity

Lazy Spilling for a Time-Predictable Stack Cache: Implementation and Analysis

The growing complexity of modern computer architectures increasingly complicates the prediction of the run-time behavior of software. For real-time systems, where a safe estimation of the program\u27s worst-case execution time is needed, time-predictable computer architectures promise to resolve this problem. A stack cache, for instance, allows the compiler to efficiently cache a program\u27s stack, while static analysis of its behavior remains easy. Likewise, its implementation requires little hardware overhead. This work introduces an optimization of the standard stack cache to avoid redundant spilling of the cache content to main memory, if the content was not modified in the meantime. At first sight, this appears to be an average-case optimization. Indeed, measurements show that the number of cache blocks spilled is reduced to about 17% and 30% in the mean, depending on the stack cache size. Furthermore, we show that lazy spilling can be analyzed with little extra effort, which benefits the worst-case spilling behavior that is relevant for a real-time system

Scratchpad Management in Software Managed Manycore Architectures

abstract: Caches have long been used to reduce memory access latency. However, the increased complexity of cache coherence brings significant challenges in processor design as the number of cores increases. While making caches scalable is still an important research problem, some researchers are exploring the possibility of a more power-efficient SRAM called scratchpad memories or SPMs. SPMs consume significantly less area, and are more energy-efficient per access than caches, and therefore make the design of on-chip memories much simpler. Unlike caches, which fetch data from memories automatically, an SPM requires explicit instructions for data transfers. SPM-only architectures are thus named as software managed manycore (SMM), since the data movements of such architectures rely on software. SMM processors have been widely used in different areas, such as embedded computing, network processing, or even high performance computing. While SMM processors provide a low-power platform, the hardware alone does not guarantee power efficiency, if applications on such processors deliver low performance. Efficient software techniques are therefore required. A big body of management techniques for SMM architectures are compiler-directed, as inserting data movement operations by hand forces programmers to trace flow of data, which can be error-prone and sometimes difficult if not impossible. This thesis develops compiler-directed techniques to manage data transfers for embedded applications on SMMs efficiently. The techniques analyze and find out the proper program points and insert data movement instructions accordingly. The techniques manage code, stack and heap data of applications, and reduce execution time by 14%, 52% and 80% respectively compared to their predecessors on typical embedded applications. On top of managing local data, a technique is also developed for shared data in SMM architectures. Experimental results show it achieves more than 2X speedup than the previous technique on average.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Dynamic Scratchpad Memory Unit for Predictable Real-Time Embedded Systems

Scratch-pad memory is a popular alternative to caches in real-time embedded systems due to its advantages in terms of timing predictability and power consumption. However, dynamic management of scratch-pad content is challenging in multitasking environments. To address this issue, this thesis proposes the design of a novel Real-Time Scratchpad Memory Unit (RSMU). The RSMU can be integrated into existing systems with minimal architectural modi cations. Furthermore, scratchpad management is performed at the OS level, requiring no application changes. In conjunction with a two-level scheduling scheme, the RSMU provides strong timing guarantees to critical tasks. Demonstration and evaluation of the system design is provided on an embedded FPGA platform

Gerenciamento energeticamente eficiente de memória para multiprocessamento em chip explorando múltiplas scratchpads

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da ComputaçãoA fim de proporcionar a alta capacidade de processamento requerida pelos dispositivos eletrônicos pessoais, sem ultrapassar os limites aceitáveis de potência e de consumo de energia, os sistemas em chip (SoCs) adotam o multiprocessamento. Para tanto, os SoCs possuem 2, 4 ou mais processadores, cada um com caches L1 privativas, conectados por meio de um barramento. Como o espaço de endereçamento visto pelos processadores é único, a programação do sistema pode assumir o modelo de memória compartilhada. A coerência entre as caches geralmente é assegurada pelo protocolo snooping. Para tirar proveito do paralelismo dos SoCs multiprocessados (MPSoCs), aplicações são desenvolvidas com uso de múltiplas threads executando concorrentemente. Neste contexto, observa-se que os dados de pilha de uma dada thread são acessados somente pelo processador no qual a thread está executando. Desta forma, a relocação da pilha para memória scratchpad (SPM) pode ser explorada para reduzir a energia do subsistema de memória. Esta redução advém não apenas da menor energia gasta em cada acesso à pilha, mas também da redução das faltas nas caches L1 de dados e da penalidade imposta pelo protocolo snooping. No presente trabalho propõe-se uma técnica para o gerenciamento dinâmico de dados de pilha em múltiplas SPMs, visando redução de energia no subsistema de memória em MPSoCs. A técnica utiliza um gerenciador totalmente em software, o qual é responsável por alocar e desalocar os dados de pilha de thread em SPM. A utilização da técnica dispensa intervenção do programador, pois as alterações necessárias no código da aplicação são realizadas por um compilador adaptado. Foram obtidos resultados experimentais através da simulação de 400 aplicações geradas aleatoriamente, assumindo-se 20 plataformas multiprocessadas, totalizando 8000 casos de uso. Os resultados mostram que, variando-se o perfil das aplicações quanto à proporção de acessos a dados de pilha, a técnica proporciona reduções de energia no subsistema de memória entre 11% e 20%, em média, para plataformas com caches L1 de 32KB, e reduções entre 14,7% e 25,9%, em média, para plataformas com caches L1 de 64KB. Para plataformas com caches L1 de menor capacidade, a redução de energia é menor pois a penalidade de faltas nas caches L1 de instruções imposta pelo gerenciador torna-se relevante

Automated Compilation Framework for Scratchpad-based Real-Time Systems

ScratchPad Memory (SPM) is highly adopted in real-time systems as it exhibits a predictable behaviour. SPM is software-managed by explicitly inserting instructions to move code and data transfers between the SPM and the main memory. However, it is a tedious job to decide how to manage the SPM and to manually modify the code to insert memory transfers. Hence, an automated compilation tool is essential to efficiently utilize the SPM. Another key problem with SPM is the latency suffered by the system due to memory transfers. Hiding this latency is important for high-performance systems. In this thesis, we address the problems of managing SPM and reducing the impact of memory latency. To realize the automation of our work, we develop a compilation framework based on the LLVM compiler to analyze and transform the program code. We exploit our framework to improve the performance of the execution of single and multi-tasks in real-time systems. For the single task execution, Worst-Case Execution Time (WCET) is of great importance to assure correct and safe behaviour of the system. So, we propose a WCET-driven allocation technique for data SPM that employs software prefetching to efficiently manage the SPM and to overlap the memory transfer and the task execution in a predictable way. On the other hand, multi-tasking requires the system to be schedulable such that all the tasks can meet their timing requirements. However, executing multiple tasks on a multi-processor platform suffers from the contention of the accesses to the shared main memory. To avoid the contention, several scheduling techniques adopted the 3-phase execution model which executes the task as a sequence of memory and computation phases. This provides the means to avoid the contention as well as to hide the memory latency by using a Direct Memory Access (DMA) engine. Executing memory transfers using the DMA allows overlapping the memory transfers with the computations on the processor. Using the 3-phase model in systems with limited sizes of local SPM may necessitate a segmentation of the task. Automating the segmentation process is necessary especially for systems with large task sets. Hence, we propose a set of efficient segmentation algorithms that follow the 3-phase execution model. The application of these algorithms shows a significant improvement in the system schedulability. For our segmentation algorithms to be more applicable, we extend the 3-phase model to allow programs with multiple paths represented as conditional Directed Acyclic Graphs (DAGs), unlike the previous works that targeted sequential programs. We also introduce a multi-steaming model to exploit the benefits of prefetching by overlapping the memory and computation phases of the same task, which was not allowed in the previous approaches. By combining the automated compilation with the proposed algorithms, we are able to achieve our goal to efficiently manage data SPM in real-time systems

Scratchpad Memory Management For Multicore Real-Time Embedded Systems

Multicore systems will continue to spread in the domain of real-time embedded systems due to the increasing need for high-performance applications. This research discusses some of the challenges associated with employing multicore systems for safety-critical real-time applications. Mainly, this work is concerned with providing: 1) efficient inter-core timing isolation for independent tasks, and 2) predictable task communication for communicating tasks. Principally, we introduce a new task execution model, based on the 3-phase execution model, that exploits the Direct Memory Access (DMA) controllers available in modern embedded platforms along with ScratchPad Memories (SPMs) to enforce strong timing isolation between tasks. The DMA and the SPMs are explicitly managed to pre-load tasks from main memory into the local (private) scratchpad memories. Tasks are then executed from the local SPMs without accessing main memory. This model allows CPU execution to be overlapped with DMA loading/unloading operations from and to main memory. We show that by co-scheduling task execution on CPUs and using DMA to access memory and I/O, we can efficiently hide access latency to physical resources. In turn, this leads to significant improvements in system schedulability, compared to both the case of unregulated contention for access to physical resources and to previous cache and SPM management techniques for real-time systems. The presented SPM-centric scheduling algorithms and analyses cover single-core, partitioned, and global real-time systems. The proposed scheme is also extended to support large tasks that do not fit entirely into the local SPM. Moreover, the schedulability analysis considers the case of recovering from transient soft errors (bit flips caused by a single event upset) in several levels of memories, that cannot be automatically corrected in hardware by the ECC unit. The proposed SPM-centric scheduling is integrated at the OS level; thus it is transparent to applications. The proposed scheme is implemented and evaluated on an FPGA platform and a Commercial-Off-The-Shelf (COTS) platform. In regards to real-time task communication, two types of communication are considered. 1) Asynchronous inter-task communication, between either sequential tasks (single-threaded) or parallel tasks (multi-threaded). 2) Intra-task communication, where parallel threads of the same application exchange data. A new task scheduling model for parallel tasks (Bundled Scheduling) is proposed to facilitate intra-task communication and reduce synchronization overheads. We show that the proposed bundled scheduling model can be applied to several parallel programming models, such as fork-join and DAG-based applications, leading to improved system schedulability. Finally, intra-task communication is governed by a predictable inter-core communication platform. Specifically, we propose HopliteRT, a lean and predictable Network-on-Chip that connects the private SPMs