A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Development of Energy Models for Design Space Exploration of Embedded Many-Core Systems

This paper introduces a methodology to develop energy models for the design space exploration of embedded many-core systems. The design process of such systems can benefit from sophisticated models. Software and hardware can be specifically optimized based on comprehensive knowledge about application scenario and hardware behavior. The contribution of our work is an automated framework to estimate the energy consumption at an arbitrary abstraction level without the need to provide further information about the system. We validated our framework with the configurable many-core system CoreVA-MPSoC. Compared to a simulation of the CoreVA-MPSoC on gate level in a 28nm FD-SOI standard cell technology, our framework shows an average estimation error of about 4%.Comment: Presented at HIP3ES, 201

Exploring Processor and Memory Architectures for Multimedia

Multimedia has become one of the cornerstones of our 21st century society and, when combined with mobility, has enabled a tremendous evolution of our society. However, joining these two concepts introduces many technical challenges. These range from having sufficient performance for handling multimedia content to having the battery stamina for acceptable mobile usage. When taking a projection of where we are heading, we see these issues becoming ever more challenging by increased mobility as well as advancements in multimedia content, such as introduction of stereoscopic 3D and augmented reality. The increased performance needs for handling multimedia come not only from an ongoing step-up in resolution going from QVGA (320x240) to Full HD (1920x1080) a 27x increase in less than half a decade. On top of this, there is also codec evolution (MPEG-2 to H.264 AVC) that adds to the computational load increase. To meet these performance challenges there has been processing and memory architecture advances (SIMD, out-of-order superscalarity, multicore processing and heterogeneous multilevel memories) in the mobile domain, in conjunction with ever increasing operating frequencies (200MHz to 2GHz) and on-chip memory sizes (128KB to 2-3MB). At the same time there is an increase in requirements for mobility, placing higher demands on battery-powered systems despite the steady increase in battery capacity (500 to 2000mAh). This leaves negative net result in-terms of battery capacity versus performance advances. In order to make optimal use of these architectural advances and to meet the power limitations in mobile systems, there is a need for taking an overall approach on how to best utilize these systems. The right trade-off between performance and power is crucial. On top of these constraints, the flexibility aspects of the system need to be addressed. All this makes it very important to reach the right architectural balance in the system. The first goal for this thesis is to examine multimedia applications and propose a flexible solution that can meet the architectural requirements in a mobile system. Secondly, propose an automated methodology of optimally mapping multimedia data and instructions to a heterogeneous multilevel memory subsystem. The proposed methodology uses constraint programming for solving a multidimensional optimization problem. Results from this work indicate that using today’s most advanced mobile processor technology together with a multi-level heterogeneous on-chip memory subsystem can meet the performance requirements for handling multimedia. By utilizing the automated optimal memory mapping method presented in this thesis lower total power consumption can be achieved, whilst performance for multimedia applications is improved, by employing enhanced memory management. This is achieved through reduced external accesses and better reuse of memory objects. This automatic method shows high accuracy, up to 90%, for predicting multimedia memory accesses for a given architecture

Doctor of Philosophy

dissertationThe embedded system space is characterized by a rapid evolution in the complexity and functionality of applications. In addition, the short time-to-market nature of the business motivates the use of programmable devices capable of meeting the conflicting constraints of low-energy, high-performance, and short design times. The keys to achieving these conflicting constraints are specialization and maximally extracting available application parallelism. General purpose processors are flexible but are either too power hungry or lack the necessary performance. Application-specific integrated circuits (ASICS) efficiently meet the performance and power needs but are inflexible. Programmable domain-specific architectures (DSAs) are an attractive middle ground, but their design requires significant time, resources, and expertise in a variety of specialties, which range from application algorithms to architecture and ultimately, circuit design. This dissertation presents CoGenE, a design framework that automates the design of energy-performance-optimal DSAs for embedded systems. For a given application domain and a user-chosen initial architectural specification, CoGenE consists of a a Compiler to generate execution binary, a simulator Generator to collect performance/energy statistics, and an Explorer that modifies the current architecture to improve energy-performance-area characteristics. The above process repeats automatically until the user-specified constraints are achieved. This removes or alleviates the time needed to understand the application, manually design the DSA, and generate object code for the DSA. Thus, CoGenE is a new design methodology that represents a significant improvement in performance, energy dissipation, design time, and resources. This dissertation employs the face recognition domain to showcase a flexible architectural design methodology that creates "ASIC-like" DSAs. The DSAs are instruction set architecture (ISA)-independent and achieve good energy-performance characteristics by coscheduling the often conflicting constraints of data access, data movement, and computation through a flexible interconnect. This represents a significant increase in programming complexity and code generation time. To address this problem, the CoGenE compiler employs integer linear programming (ILP)-based 'interconnect-aware' scheduling techniques for automatic code generation. The CoGenE explorer employs an iterative technique to search the complete design space and select a set of energy-performance-optimal candidates. When compared to manual designs, results demonstrate that CoGenE produces superior designs for three application domains: face recognition, speech recognition and wireless telephony. While CoGenE is well suited to applications that exhibit a streaming behavior, multithreaded applications like ray tracing present a different but important challenge. To demonstrate its generality, CoGenE is evaluated in designing a novel multicore N-wide SIMD architecture, known as StreamRay, for the ray tracing domain. CoGenE is used to synthesize the SIMD execution cores, the compiler that generates the application binary, and the interconnection subsystem. Further, separating address and data computations in space reduces data movement and contention for resources, thereby significantly improving performance compared to existing ray tracing approaches

ASAM : Automatic Architecture Synthesis and Application Mapping; dl. 3.2: Instruction set synthesis

No abstract

The use of a reconfigurable functional cache in a digital signal processor: power and performance

Due to the computationally intensive nature of the tasks that digital signal processors (DSP) are required to perform it is desirable to decrease the time required to execute these tasks. Minimizing the execution time required for the various algorithms that are commonly and frequently executed (ex: FIR filters) will improve the overall performance. It is known that hardware is able to execute algorithms faster than software, however, due to the size limitations of embedded DSP, not all of the necessary algorithms can be implemented in hardware. A reconfigurable cache architecture in combination with a DSP is proposed as an alternative to increase algorithm performance by using reconfigurable hardware rather than dedicated hardware. Another important issue to consider for embedded processors is the power consumption of the DSP. Due to the fact that most embedded processors operate by battery power, energy efficiency is a necessity. This study looks at the power requirements of a DSP with reconfigurable cache to determine the viability of such an architecture in an embedded system. Others have shown that reconfigurable cache in conjunction with a general purpose processor improves performance for some DSP benchmarks. This study shows that a DSP/reconfigurable cache combination can achieve kernel performance gains ranging from 10-350 times that of a DSP architecture operating alone and can achieve overall benchmark speedups ranging from 1.02 to 1.91 times that of the existing DSP architecture. Further, relative power consumption results show that the power consumption of the reconfigurable architecture is approximately 85 to 95% of the current architecture (5-15% power savings) and attains energy savings ranging from approximately 14 to 50%

Combining FPGA prototyping and high-level simulation approaches for Design Space Exploration of MPSoCs

Modern embedded systems are parallel, component-based, heterogeneous and finely tuned on the basis of the workload that must be executed on them. To improve design reuse, Application Specific Instruction-set Processors (ASIPs) are often employed as building blocks in such systems, as a solution capable of satisfying the required functional and physical constraints (e.g. throughput, latency, power or energy consumption etc.), while providing, at the same time, high flexibility and adaptability. Composing a multi-processor architecture including ASIPs and mapping parallel applications onto it is a design activity that require an extensive Design Space Exploration process (DSE), to result in cost-effective systems. The work described here aims at defining novel methodologies for the application-driven customizations of such highly heterogeneous embedded systems. The issue is tackled at different levels, integrating different tools. High-level event-based simulation is a widely used technique that offers speed and flexibility as main points of strength, but needs, as a preliminary input and periodically during the iteration process, calibration data that must be acquired by means of more accurate evaluation methods. Typically, this calibration is performed using instruction-level cycleaccurate simulators that, however, turn out to be very slow, especially when complete multiprocessor systems must be evaluated or when the grain of the calibration is too fine, while FPGA approaches have shown to performbetter for this particular applications. FPGA-based emulation techniques have been proposed in the recent past as an alternative solution to the software-based simulation approach, but some further steps are needed before they can be effectively exploitedwithin architectural design space exploration. Firstly, some kind of technology-awareness must be introduced, to enable the translation of the emulation results into a pre-estimation of a prospective ASIC implementation of the design. Moreover, when performing architectural DSE, a significant number of different candidate design points has to be evaluated and compared. In this case, if no countermeasures are taken, the advantages achievable with FPGAs, in terms of emulation speed, are counterbalanced by the overhead introduced by the time needed to go through the physical synthesis and implementation flow. Developed FPGA-based prototyping platform overcomes such limitations, enabling the use of FPGA-based prototyping for micro-architectural design space exploration of ASIP processors. In this approach, to increase the emulation speed-up, two different methods are proposed: the first is based on automatic instantiation of additional hardware modules, able to reconfigure at runtime the prototype, while the second leverages manipulation of application binary code, compiled for a custom VLIW ASIP architecture, that is transformed into code executable on a different configuration. This allows to prototype a whole set of ASIP solutions after one single FPGA implementation flow, mitigating the afore-mentioned overhead.A short overview on the tools used throughout the work will also be offered, covering basic aspects of Intel-Silicon Hive ASIP development toolchain, SESAME framework general description, along with a review of state-of-art simulation and prototyping techniques for complex multi-processor systems. Each proposed approach will be validated through a real-world use case, confirming the validity of this solution