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

Parallelism-Aware Memory Interference Delay Analysis for COTS Multicore Systems

In modern Commercial Off-The-Shelf (COTS) multicore systems, each core can generate many parallel memory requests at a time. The processing of these parallel requests in the DRAM controller greatly affects the memory interference delay experienced by running tasks on the platform. In this paper, we model a modern COTS multicore system which has a nonblocking last-level cache (LLC) and a DRAM controller that prioritizes reads over writes. To minimize interference, we focus on LLC and DRAM bank partitioned systems. Based on the model, we propose an analysis that computes a safe upper bound for the worst-case memory interference delay. We validated our analysis on a real COTS multicore platform with a set of carefully designed synthetic benchmarks as well as SPEC2006 benchmarks. Evaluation results show that our analysis is more accurately capture the worst-case memory interference delay and provides safer upper bounds compared to a recently proposed analysis which significantly under-estimate the delay.Comment: Technical Repor

The "MIND" Scalable PIM Architecture

MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture