Multi-core Architectures and Streaming Applications

In this paper we focus on algorithms and reconfigurable multi-core architectures for streaming digital signal processing (DSP) applications. The multi-core concept has a number of advantages: (1) depending on the requirements more or fewer cores can be switched on/off, (2) the multi-core structure fits well to future process technologies, more cores will be available in advanced process technologies, but the complexity per core does not increase, (3) the multi-core concept is fault tolerant, faulty cores can be discarded and (4) multiple cores can be configured fast in parallel. Because in our approach processing and memory are combined in the cores, tasks can be executed efficiently on cores (locality of reference). There are a number of application domains that can be considered as streaming DSP applications: for example wireless baseband processing (for HiperLAN/2, WiMax, DAB, DRM, and DVB), multimedia processing (e.g. MPEG, MP3 coding/decoding), medical image processing, colour image processing, sensor processing (e.g. remote surveillance cameras) and phased array radar systems. In this paper the key characteristics of streaming DSP applications are highlighted, and the characteristics of the processing architectures to efficiently support these types of applications are addressed. We present the initial results of the Annabelle chip that we designed with our approach

FastFlow: Efficient Parallel Streaming Applications on Multi-core

Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.Comment: 23 pages + cove

Cache-Conscious Offline Real-Time Task Scheduling for Multi-Core Processors

Most schedulability analysis techniques for multi-core architectures assume a single Worst-Case Execution Time (WCET) per task, which is valid in all execution conditions. This assumption is too pessimistic for parallel applications running on multi-core architectures with local instruction or data caches, for which the WCET of a task depends on the cache contents at the beginning of its execution, itself depending on the task that was executed before the task under study. In this paper, we propose two scheduling techniques for multi-core architectures equipped with local instruction and data caches. The two techniques schedule a parallel application modeled as a task graph, and generate a static partitioned non-preemptive schedule. We propose an optimal method, using an Integer Linear Programming (ILP) formulation, as well as a heuristic method based on list scheduling. Experimental results show that by taking into account the effect of private caches on tasks\u27 WCETs, the length of generated schedules is significantly reduced as compared to schedules generated by cache-unaware scheduling methods. The observed schedule length reduction on streaming applications is 11% on average for the optimal method and 9% on average for the heuristic method

Design and optimization of a portable LQCD Monte Carlo code using OpenACC

The present panorama of HPC architectures is extremely heterogeneous, ranging from traditional multi-core CPU processors, supporting a wide class of applications but delivering moderate computing performance, to many-core GPUs, exploiting aggressive data-parallelism and delivering higher performances for streaming computing applications. In this scenario, code portability (and performance portability) become necessary for easy maintainability of applications; this is very relevant in scientific computing where code changes are very frequent, making it tedious and prone to error to keep different code versions aligned. In this work we present the design and optimization of a state-of-the-art production-level LQCD Monte Carlo application, using the directive-based OpenACC programming model. OpenACC abstracts parallel programming to a descriptive level, relieving programmers from specifying how codes should be mapped onto the target architecture. We describe the implementation of a code fully written in OpenACC, and show that we are able to target several different architectures, including state-of-the-art traditional CPUs and GPUs, with the same code. We also measure performance, evaluating the computing efficiency of our OpenACC code on several architectures, comparing with GPU-specific implementations and showing that a good level of performance-portability can be reached.Comment: 26 pages, 2 png figures, preprint of an article submitted for consideration in International Journal of Modern Physics

HSTREAM: A directive-based language extension for heterogeneous stream computing

Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require advanced knowledge of several hardware architectures and device-specific programming models, including OpenMP and CUDA. In this paper, we present HSTREAM, a compiler directive-based language extension to support programming stream computing applications for heterogeneous parallel computing systems. HSTREAM source-to-source compiler aims to increase the programming productivity by enabling programmers to annotate the parallel regions for heterogeneous execution and generate target specific code. The HSTREAM runtime automatically distributes the workload across CPUs and accelerating devices. We demonstrate the usefulness of HSTREAM language extension with various applications from the STREAM benchmark. Experimental evaluation results show that HSTREAM can keep the same programming simplicity as OpenMP, and the generated code can deliver performance beyond what CPUs-only and GPUs-only executions can deliver.Comment: Preprint, 21st IEEE International Conference on Computational Science and Engineering (CSE 2018

A Power-Aware Framework for Executing Streaming Programs on Networks-on-Chip

Nilesh Karavadara, Simon Folie, Michael Zolda, Vu Thien Nga Nguyen, Raimund Kirner, 'A Power-Aware Framework for Executing Streaming Programs on Networks-on-Chip'. Paper presented at the Int'l Workshop on Performance, Power and Predictability of Many-Core Embedded Systems (3PMCES'14), Dresden, Germany, 24-28 March 2014.Software developers are discovering that practices which have successfully served single-core platforms for decades do no longer work for multi-cores. Stream processing is a parallel execution model that is well-suited for architectures with multiple computational elements that are connected by a network. We propose a power-aware streaming execution layer for network-on-chip architectures that addresses the energy constraints of embedded devices. Our proof-of-concept implementation targets the Intel SCC processor, which connects 48 cores via a network-on- chip. We motivate our design decisions and describe the status of our implementation

Worst-Case Communication Overhead in a Many-Core based Shared-Memory Model

National audienceWith emerging many-core architectures, using on-chip shared memories is an interesting approach because it provides high bandwidth and high throughput data exchange. Such a feature is usually implemented as a multi-bus multi-banked memory. Since predicting timing behavior is key to efficient design and verification of embedded real-time systems, the question that arises is how to evaluate the access time for one memory access of a given task while others may concurrently access the same memory-bank at t the same time. In this paper, we give the answers for a subset of streaming applications modeled like CSDF Model of Computation and implemented in Kalray’s MPPA chip