ﻻ يوجد ملخص باللغة العربية
Hardware specialization is becoming a key enabler of energyefficient performance. Future systems will be increasingly heterogeneous, integrating multiple specialized and programmable accelerators, each with different memory demands. Traditionally, communication between accelerators has been inefficient, typically orchestrated through explicit DMA transfers between different address spaces. More recently, industry has proposed unified coherent memory which enables implicit data movement and more data reuse, but often these interfaces limit the coherence flexibility available to heterogeneous systems. This paper demonstrates the benefits of fine-grained coherence specialization for heterogeneous systems. We propose an architecture that enables low-complexity independent specialization of each individual coherence request in heterogeneous workloads by building upon a simple and flexible baseline coherence interface, Spandex. We then describe how to optimize individual memory requests to improve cache reuse and performance-critical memory latency in emerging heterogeneous workloads. Collectively, our techniques enable significant gains, reducing execution time by up to 61% or network traffic by up to 99% while adding minimal complexity to the Spandex protocol.
We propose the first Reversible Coherence Protocol (RCP), a new protocol designed from ground up that enables invisible speculative load. RCP takes a bold approach by including the speculative loads and merge/purge operation in the interface between
One of the most critical aspects of integrating loosely-coupled accelerators in heterogeneous SoC architectures is orchestrating their interactions with the memory hierarchy, especially in terms of navigating the various cache-coherence options: from
While multi-GPU (MGPU) systems are extremely popular for compute-intensive workloads, several inefficiencies in the memory hierarchy and data movement result in a waste of GPU resources and difficulties in programming MGPU systems. First, due to the
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in peoples lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edg
The use of multi-chip modules (MCM) and/or multi-socket boards is the most suitable approach to increase the computation density of servers while keep chip yield attained. This paper introduces a new coherence protocol suitable, in terms of complexit