Transformations of High-Level Synthesis Codes for High-Performance Computing

Specialized hardware architectures promise a major step in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing systems. The adoption of high-level synthesis (HLS) from languages such as C/C++ and OpenCL has greatly increased programmer productivity when designing for such platforms. While this has enabled a wider audience to target specialized hardware, the optimization principles known from traditional software design are no longer sufficient to implement high-performance codes. Fast and efficient codes for reconfigurable platforms are thus still challenging to design. To alleviate this, we present a set of optimizing transformations for HLS, targeting scalable and efficient architectures for high-performance computing (HPC) applications. Our work provides a toolbox for developers, where we systematically identify classes of transformations, the characteristics of their effect on the HLS code and the resulting hardware (e.g., increases data reuse or resource consumption), and the objectives that each transformation can target (e.g., resolve interface contention, or increase parallelism). We show how these can be used to efficiently exploit pipelining, on-chip distributed fast memory, and on-chip streaming dataflow, allowing for massively parallel architectures. To quantify the effect of our transformations, we use them to optimize a set of throughput-oriented FPGA kernels, demonstrating that our enhancements are sufficient to scale up parallelism within the hardware constraints. With the transformations covered, we hope to establish a common framework for performance engineers, compiler developers, and hardware developers, to tap into the performance potential offered by specialized hardware architectures using HLS

Optimizing Performance and Scalability on Hybrid MPSoCs

Hardware accelerators are capable of achieving significant performance improvement. But design- ing hardware accelerators lacks the flexibility and the productivity. Combining hardware accelerators with multiprocessor system-on-chip (MPSoC) is an alternative way to balance the flexibility, the productivity, and the performance. However, without appropriate programming model it is still a challenge to achieve parallelism on a hybrid (MPSoC) with with both general-purpose processors and dedicated accelerators. Besides, increasing computation demands with limited power budget require more energy-efficient design without performance degradation in embedded systems and mobile computing platforms. Reconfigurable computing with emerging storage technologies is an alternative to enable the optimization of both performance and power consumption. In this work, we present a hybrid OpenCL-like (HOpenCL) parallel computing framework on FPGAs. The hybrid hardware platform as well as both the hardware and software kernels can be generated through this an automatic design flow. In addition, the OpenCL-like programming model is exploited to combine software and hardware kernels running on the unified hardware platform. By using the partial reconfiguration technique, a dynamic reconfiguration scheme is presented to optimize performance without losing the programmable flexibility. Our results show that our automatic design flow can not only significantly minimize the development time, but also gain about 11 times speedup compared with pure software parallel implementation. When partial reconfiguration is enable to conduct dynamic scheduling, the overall performance speedup of our mixed micro benchmarks is around 5.2 times

AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture

CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. These architectures provide programmers with the ability to reprogram the FPGAs for flexible acceleration of many workloads. Nonetheless, this advantage is often overshadowed by the poor programmability of FPGAs whose programming is conventionally a RTL design practice. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. This paper aims to address this challenge and pave the path from software programs towards high-quality FPGA accelerators. Specifically, we first propose the composable, parallel and pipeline (CPP) microarchitecture as a template of accelerator designs. Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space. Also, we introduce an analytical model to capture the performance and resource trade-offs among different design configurations of the CPP microarchitecture, which lays the foundation for fast design space exploration. On top of the CPP microarchitecture and its analytical model, we develop the AutoAccel framework to make the entire accelerator generation automated. AutoAccel accepts a software program as an input and performs a series of code transformations based on the result of the analytical-model-based design space exploration to construct the desired CPP microarchitecture. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels

Cognitive Radio Programming: Existing Solutions and Open Issues

Software defined radio (sdr) technology has evolved rapidly and is now reaching market maturity, providing solutions for cognitive radio applications. Still, a lot of issues have yet to be studied. In this paper, we highlight the constraints imposed by recent radio protocols and we present current architectures and solutions for programming sdr. We also list the challenges to overcome in order to reach mastery of future cognitive radios systems.La radio logicielle a évolué rapidement pour atteindre la maturité nécessaire pour être mise sur le marché, offrant de nouvelles solutions pour les applications de radio cognitive. Cependant, beaucoup de problèmes restent à étudier. Dans ce papier, nous présentons les contraintes imposées par les nouveaux protocoles radios, les architectures matérielles existantes ainsi que les solutions pour les programmer. De plus, nous listons les difficultés à surmonter pour maitriser les futurs systèmes de radio cognitive

Study of combining GPU/FPGA accelerators for high-performance computing

This contribution presents the performance modeling of a super desktop with GPU and FPGA accelerators, using OpenCL for the GPU and a high-level synthesis compiler for the FPGAs. The performance model is used to evaluate the different high-level synthesis optimizations, taking into account the resource usage, and to compare the compute power of the FPGA with the GP

An OpenCL software compilation framework targeting an SoC-FPGA VLIW chip multiprocessor

Modern systems-on-chip augment their baseline CPU with coprocessors and accelerators to increase overall computational capability and power efficiency, and thus have evolved into heterogeneous multi-core systems. Several languages have been developed to enable this paradigm shift, including CUDA and OpenCL. This paper discusses a unified compilation environment to enable heterogeneous system design through the use of OpenCL and a highly configurable VLIW Chip Multiprocessor architecture known as the LE1. An LLVM compilation framework was researched and a prototype developed to enable the execution of OpenCL applications on a number of hardware configurations of the LE1 CMP. The presented OpenCL framework fully automates the compilation flow and supports work-item coalescing which better maps onto the ILP processor cores of the LE1 architecture. This paper discusses in detail both the software stack and target hardware architecture and evaluates the scalability of the proposed framework by running 12 industry-standard OpenCL benchmarks drawn from the AMD SDK and the Rodinia suites. The benchmarks are executed on 40 LE1 configurations with 10 implemented on an SoC-FPGA and the remaining on a cycle-accurate simulator. Across 12 OpenCL benchmarks results demonstrate near-linear wall-clock performance improvement of 1.8x (using 2 dual-issue cores), up to 5.2x (using 8 dual-issue cores) and on one case, super-linear improvement of 8.4x (FixOffset kernel, 8 dual-issue cores). The number of OpenCL benchmarks evaluated makes this study one of the most complete in the literature