Data-level parallelism is frequently ignored or underutilized. Achieved through vector/SIMD capabilities, it can provide substantial performance improvements on top of widely used techniques such as thread-level parallelism. However, manual vectorization is a tedious and costly process that needs to be repeated for each specific instruction set or register size. In addition, automatic compiler vectorization is susceptible to code complexity, and usually limited due to data and control dependencies. To address some these issues, Arm recently released a new vector ISA, the Scalable Vector Extension (SVE), which is Vector-Length Agnostic (VLA). VLA enables the generation of binary files that run regardless of the physical vector register length.

The Modular Microserver DataCentre (M2DC) project will investigate, develop and demonstrate a modular, highly-efficient, cost-optimized server architecture composed of heterogeneous microserver computing resources, being able to be tailored to meet requirements from various application domains such as image processing, cloud computing or HPC. M2DC will be built on three main pillars: a flexible server architecture that can be easily customised, maintained and updated, advanced management strategies and system efficiency enhancements (SEE), well-defined interfaces to surrounding software data centre ecosystem.

High-performance computing (HPC) is recognized as one of the pillars for further progress in science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging architectural challenges in order to reach Exascale level of performance, projected for the year 2020. The much larger embedded and mobile market allows for rapid development of intellectual property (IP) blocks and provides more flexibility in designing an application-specific system-on-chip (SoC), in turn providing the possibility in balancing performance, energy-efficiency, and cost. In the Mont-Blanc project, we advocate for HPC systems being built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such an approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and network interface cards (NICs) from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling, and integration solutions. We present the system's architecture and evaluate both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability and estimate the maximum scalability point of this approach across a set of applications.

A large portion of existing multithreaded embedded software has been programmed according to symmetric shared memory platforms where a monolithic memory block is shared by all cores. Such platforms accommodate popular parallel programming models such as POSIX threads and OpenMP. However with the growing number of cores in modern manycore embedded architectures, they present a bottleneck related to their centralized memory accesses. This paper proposes a solution tailored for an efficient execution of applications defined with shared-memory programming models onto on-chip distributed-memory multicore architectures. It shows how performance, area and energy consumption are significantly improved thanks to the scalability of these architectures. This is illustrated in an open-source realistic design framework, including tools from ASIC to microkernel.

International audience

Scalability and programmability are important issues in large homogeneous MPSoCs. Such architectures often rely on explicit message-passing among processors, each of which possessing a local private memory. This paper presents a low-overhead hardware/software distributed shared memory approach that makes such architectures multithreading-capable. The proposed solution is implemented into an open-source message-passing MPSoC through developing a POSIX-like thread API, which shows excellent scalability using application kernels used for benchmarking in shared-memory systems. This approach efficiently draws strengths from the on-chip distributed private memory that opens the way to exposing the multithreading programmability/capabilities of that component as a general-purpose accelerator.