Automatic generation of synthetic workloads for multicore systems

textWhen designing a computer system, benchmark programs are used with cycle accurate performance/power simulators and HDL level simulators to evaluate novel architectural enhancements, perform design space exploration, understand the worst-case power characteristics of various designs and find performance bottlenecks. This research effort is directed towards automatically generating synthetic benchmarks to tackle three design challenges: 1) For most of the simulation related purposes, full runs of modern real world parallel applications like the PARSEC, SPLASH suites cannot be used as they take machine weeks of time on cycle accurate and HDL level simulators incurring a prohibitively large time cost 2) The second design challenge is that, some of these real world applications are intellectual property and cannot be shared with processor vendors for design studies 3) The most significant problem in the design stage is the complexity involved in fixing the maximum power consumption of a multicore design, called the Thermal Design Power (TDP). In an effort towards fixing this maximum power consumption of a system at the most optimal point, designers are used to hand-crafting possible code snippets called power viruses. But, this process of trying to manually write such maximum power consuming code snippets is very tedious. All of these aforementioned challenges has lead to the resurrection of synthetic benchmarks in the recent past, serving as a promising solution to all the challenges. During the design stage of a multicore system, availability of a framework to automatically generate system-level synthetic benchmarks for multicore systems will greatly simplify the design process and result in more confident design decisions. The key idea behind such an adaptable benchmark synthesis framework is to identify the key characteristics of real world parallel applications that affect the performance and power consumption of a real program and create synthetic executable programs by varying the values for these characteristics. Firstly, with such a framework, one can generate miniaturized synthetic clones for large target (current and futuristic) parallel applications enabling an architect to use them with slow low-level simulation models (e.g., RTL models in VHDL/Verilog) and helps in tailoring designs to the targeted applications. These synthetic benchmark clones can be distributed to architects and designers even if the original applications are intellectual property, when they are not publicly available. Lastly, such a framework can be used to automatically create maximum power consuming code snippets to be able to help in fixing the TDP, heat sinks, cooling system and other power related features of the system. The workload cloning framework built using the proposed synthetic benchmark generation methodology is evaluated to show its superiority over the existing cloning methodologies for single-core systems by generating miniaturized clones for CPU2006 and ImplantBench workloads with only an average error of 2.9% in performance for up to five orders of magnitude of simulation speedup. The correlation coefficient predicting the sensitivity to design changes is 0.95 and 0.98 for performance and power consumption. The proposed framework is evaluated by cloning parallel applications implemented based on p-threads and OpenMP in the PARSEC benchmark suite. The average error in predicting performance is 4.87% and that of power consumption is 2.73%. The correlation coefficient predicting the sensitivity to design changes is 0.92 for performance. The efficacy of the proposed synthetic benchmark generation framework for power virus generation is evaluation on SPARC, Alpha and x86 ISAs using full system simulators and also using real hardware. The results show that the power viruses generated for single-core systems consume 14-41% more power compared to MPrime on SPARC ISA. Similarly, the power viruses generated for multicore systems consume 45-98%, 40-89% and 41-56% more power than PARSEC workloads, running multiple copies of MPrime and multithreaded SPECjbb respectively.Electrical and Computer Engineerin

Simulating Operational Concepts for Autonomous Robotic Space Exploration Systems: A Framework for Early Design Validation

During mission design, the concept of operations (ConOps) describes how the system operates during various life cycle phases to meet stakeholder expectations. ConOps is sometimes declined in a simple evaluation of the power consumption or data generation per mode. Different operational timelines are typically developed based on expert knowledge. This approach is robust when designing an automated system or a system with a low level of autonomy. However, when studying highly autonomous systems, designers may be interested in understanding how the system would react in an operational scenario when provided with knowledge about its actions and operational environment. These considerations can help verify and validate the proposed ConOps architecture, highlight shortcomings in both physical and functional design, and help better formulate detailed requirements. Hence, this study aims to provide a framework for the simulation and validation of operational scenarios for autonomous robotic space exploration systems during the preliminary design phases. This study extends current efforts in autonomy technology for planetary systems by focusing on testing their operability and assessing their performances in different scenarios early in the design process. The framework uses Model-Based Systems Engineering (MBSE) as the knowledge base for the studied system and its operations. It then leverages a Markov Decision Process (MDP) to simulate a set of system operations in a relevant scenario. It then outputs a feasible plan with the associated variation of a set of considered resources as step functions. This method was applied to simulate the operations of a small rover exploring an unknown environment to observe and sample a set of targets

High-level synthesis of dataflow programs for heterogeneous platforms:design flow tools and design space exploration

The growing complexity of digital signal processing applications implemented in programmable logic and embedded processors make a compelling case the use of high-level methodologies for their design and implementation. Past research has shown that for complex systems, raising the level of abstraction does not necessarily come at a cost in terms of performance or resource requirements. As a matter of fact, high-level synthesis tools supporting such a high abstraction often rival and on occasion improve low-level design. In spite of these successes, high-level synthesis still relies on programs being written with the target and often the synthesis process, in mind. In other words, imperative languages such as C or C++, most used languages for high-level synthesis, are either modified or a constrained subset is used to make parallelism explicit. In addition, a proper behavioral description that permits the unification for hardware and software design is still an elusive goal for heterogeneous platforms. A promising behavioral description capable of expressing both sequential and parallel application is RVC-CAL. RVC-CAL is a dataflow programming language that permits design abstraction, modularity, and portability. The objective of this thesis is to provide a high-level synthesis solution for RVC-CAL dataflow programs and provide an RVC-CAL design flow for heterogeneous platforms. The main contributions of this thesis are: a high-level synthesis infrastructure that supports the full specification of RVC-CAL, an action selection strategy for supporting parallel read and writes of list of tokens in hardware synthesis, a dynamic fine-grain profiling for synthesized dataflow programs, an iterative design space exploration framework that permits the performance estimation, analysis, and optimization of heterogeneous platforms, and finally a clock gating strategy that reduces the dynamic power consumption. Experimental results on all stages of the provided design flow, demonstrate the capabilities of the tools for high-level synthesis, software hardware Co-Design, design space exploration, and power optimization for reconfigurable hardware. Consequently, this work proves the viability of complex systems design and implementation using dataflow programming, not only for system-level simulation but real heterogeneous implementations

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

FASTCUDA: Open Source FPGA Accelerator & Hardware-Software Codesign Toolset for CUDA Kernels

Using FPGAs as hardware accelerators that communicate with a central CPU is becoming a common practice in the embedded design world but there is no standard methodology and toolset to facilitate this path yet. On the other hand, languages such as CUDA and OpenCL provide standard development environments for Graphical Processing Unit (GPU) programming. FASTCUDA is a platform that provides the necessary software toolset, hardware architecture, and design methodology to efficiently adapt the CUDA approach into a new FPGA design flow. With FASTCUDA, the CUDA kernels of a CUDA-based application are partitioned into two groups with minimal user intervention: those that are compiled and executed in parallel software, and those that are synthesized and implemented in hardware. A modern low power FPGA can provide the processing power (via numerous embedded micro-CPUs) and the logic capacity for both the software and hardware implementations of the CUDA kernels. This paper describes the system requirements and the architectural decisions behind the FASTCUDA approach

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201