ACOTES project: Advanced compiler technologies for embedded streaming

Streaming applications are built of data-driven, computational components, consuming and producing unbounded data streams. Streaming oriented systems have become dominant in a wide range of domains, including embedded applications and DSPs. However, programming efficiently for streaming architectures is a challenging task, having to carefully partition the computation and map it to processes in a way that best matches the underlying streaming architecture, taking into account the distributed resources (memory, processing, real-time requirements) and communication overheads (processing and delay). These challenges have led to a number of suggested solutions, whose goal is to improve the programmer’s productivity in developing applications that process massive streams of data on programmable, parallel embedded architectures. StreamIt is one such example. Another more recent approach is that developed by the ACOTES project (Advanced Compiler Technologies for Embedded Streaming). The ACOTES approach for streaming applications consists of compiler-assisted mapping of streaming tasks to highly parallel systems in order to maximize cost-effectiveness, both in terms of energy and in terms of design effort. The analysis and transformation techniques automate large parts of the partitioning and mapping process, based on the properties of the application domain, on the quantitative information about the target systems, and on programmer directives. This paper presents the outcomes of the ACOTES project, a 3-year collaborative work of industrial (NXP, ST, IBM, Silicon Hive, NOKIA) and academic (UPC, INRIA, MINES ParisTech) partners, and advocates the use of Advanced Compiler Technologies that we developed to support Embedded Streaming.Peer ReviewedPostprint (published version

Automating the construction of a complier heuristics using machine learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 153-162).Compiler writers are expected to create effective and inexpensive solutions to NP-hard problems such as instruction scheduling and register allocation. To make matters worse, separate optimization phases have strong interactions and competing resource constraints. Compiler writers deal with system complexity by dividing the problem into multiple phases and devising approximate heuristics for each phase. However, to achieve satisfactory performance, developers are forced to manually tweak their heuristics with trial-and-error experimentation. In this dissertation I present meta optimization, a methodology for automatically constructing high quality compiler heuristics using machine learning techniques. This thesis describes machine-learned heuristics for three important compiler optimizations: hyperblock formation, register allocation, and loop unrolling. The machine-learned heuristics outperform (by as much as 3x in some cases) their state-of-the-art hand-crafted counterparts. By automatically collecting data and systematically analyzing them, my techniques discover subtle interactions that even experienced engineers would likely overlook. In addition to improving performance, my techniques can significantly reduce the human effort involved in compiler design.(cont.) Machine learning algorithms can design critical portions of compiler heuristics, thereby freeing the human designer to focus on compiler correctness. The progression of experiments I conduct in this thesis leads to collaborative compilation, an approach which enables ordinary users to transparently train compiler heuristics by running their applications as they normally would. The collaborative system automatically adapts itself to the applications in which a community of users is interested.by Mark W. Stephenson.Ph.D

WCET-Aware Scratchpad Memory Management for Hard Real-Time Systems

abstract: Cyber-physical systems and hard real-time systems have strict timing constraints that specify deadlines until which tasks must finish their execution. Missing a deadline can cause unexpected outcome or endanger human lives in safety-critical applications, such as automotive or aeronautical systems. It is, therefore, of utmost importance to obtain and optimize a safe upper bound of each task’s execution time or the worst-case execution time (WCET), to guarantee the absence of any missed deadline. Unfortunately, conventional microarchitectural components, such as caches and branch predictors, are only optimized for average-case performance and often make WCET analysis complicated and pessimistic. Caches especially have a large impact on the worst-case performance due to expensive off- chip memory accesses involved in cache miss handling. In this regard, software-controlled scratchpad memories (SPMs) have become a promising alternative to caches. An SPM is a raw SRAM, controlled only by executing data movement instructions explicitly at runtime, and such explicit control facilitates static analyses to obtain safe and tight upper bounds of WCETs. SPM management techniques, used in compilers targeting an SPM-based processor, determine how to use a given SPM space by deciding where to insert data movement instructions and what operations to perform at those program locations. This dissertation presents several management techniques for program code and stack data, which aim to optimize the WCETs of a given program. The proposed code management techniques include optimal allocation algorithms and a polynomial-time heuristic for allocating functions to the SPM space, with or without the use of abstraction of SPM regions, and a heuristic for splitting functions into smaller partitions. The proposed stack data management technique, on the other hand, finds an optimal set of program locations to evict and restore stack frames to avoid stack overflows, when the call stack resides in a size-limited SPM. In the evaluation, the WCETs of various benchmarks including real-world automotive applications are statically calculated for SPMs and caches in several different memory configurations.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Characterizing function inlining with genetic programming

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 74-75).Function inlining is a compiler optimization where the function call is replaced by the code from the function itself. Using a form of machine learning called genetic programming, this thesis examines which factors are important in determining which function calls to inline to maximize performance. A number of different heuristics are generated for inlining decisions in the Trimaran compiler, which improve on performance from the current default inlining heuristic. Also, trends in function inlining are examined over the thousands of compilation runs that are completed.by Chris Yu.M.Eng

Dynamic optimization through the use of automatic runtime specialization

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 99-115).by John Whaley.S.B.and M.Eng

From Parallel Programs to Customized Parallel Processors

The need for fast time to market of new embedded processor-based designs calls for a rapid design methodology of the included processors. The call for such a methodology is even more emphasized in the context of so called soft cores targeted to reconfigurable fabrics where per-design processor customization is commonplace. The C language has been commonly used as an input to hardware/software co-design flows. However, as C is a sequential language, its potential to generate parallel operations to utilize naturally parallel hardware constructs is far from optimal, leading to a customized processor design space with limited parallel resource scalability. In contrast, when utilizing a parallel programming language as an input, a wider processor design space can be explored to produce customized processors with varying degrees of utilized parallelism. This Thesis proposes a novel Multicore Application-Specific Instruction Set Processor (MCASIP) co-design methodology that exploits parallel programming languages as the application input format. In the methodology, the designer can explicitly capture the parallelism of the algorithm and exploit specialized instructions using a parallel programming language in contrast to being on the mercy of the compiler or the hardware to extract the parallelism from a sequential input. The Thesis proposes a multicore processor template based on the Transport Triggered Architecture, compiler techniques involved in static parallelization of computation kernels with barriers and a datapath integrated hardware accelerator for low overhead software synchronization implementation. These contributions enable scaling the customized processors both at the instruction and task levels to efficiently exploit the parallelism in the input program up to the implementation constraints such as the memory bandwidth or the chip area. The different contributions are validated with case studies, comparisons and design examples

Dynamic Binary Translation for Embedded Systems with Scratchpad Memory

Embedded software development has recently changed with advances in computing. Rather than fully co-designing software and hardware to perform a relatively simple task, nowadays embedded and mobile devices are designed as a platform where multiple applications can be run, new applications can be added, and existing applications can be updated. In this scenario, traditional constraints in embedded systems design (i.e., performance, memory and energy consumption and real-time guarantees) are more difficult to address. New concerns (e.g., security) have become important and increase software complexity as well. In general-purpose systems, Dynamic Binary Translation (DBT) has been used to address these issues with services such as Just-In-Time (JIT) compilation, dynamic optimization, virtualization, power management and code security. In embedded systems, however, DBT is not usually employed due to performance, memory and power overhead. This dissertation presents StrataX, a low-overhead DBT framework for embedded systems. StrataX addresses the challenges faced by DBT in embedded systems using novel techniques. To reduce DBT overhead, StrataX loads code from NAND-Flash storage and translates it into a Scratchpad Memory (SPM), a software-managed on-chip SRAM with limited capacity. SPM has similar access latency as a hardware cache, but consumes less power and chip area. StrataX manages SPM as a software instruction cache, and employs victim compression and pinning to reduce retranslation cost and capture frequently executed code in the SPM. To prevent performance loss due to excessive code expansion, StrataX minimizes the amount of code inserted by DBT to maintain control of program execution. When a hardware instruction cache is available, StrataX dynamically partitions translated code among the SPM and main memory. With these techniques, StrataX has low performance overhead relative to native execution for MiBench programs. Further, it simplifies embedded software and hardware design by operating transparently to applications without any special hardware support. StrataX achieves sufficiently low overhead to make it feasible to use DBT in embedded systems to address important design goals and requirements