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Sriram Aananthakrishnan

Publications and source records attributed to Sriram Aananthakrishnan.

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PIUMA: Programmable Integrated Unified Memory Architecture

High performance large scale graph analytics are essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on those workloads. To enable efficient and scalable graph analysis, Intel developed the Programmable Integrated Unified Memory Architecture (PIUMA) as a part of the DARPA Hierarchical Identify Verify Exploit (HIVE) program. PIUMA consists of many multi-threaded cores, fine-grained memory and network accesses, a globally shared address space, powerful offload engines and a tightly integrated optical interconnection network. By utilizing co-packaged optical silicon photonics and extending the on-chip mesh protocol directly to the optical fabric, all PIUMA chips in a system are glued together in a large virtual die which allows for extremely low socket-to-socket latencies even as the system scales to thousands of sockets. Performance estimations project that a PIUMA node will outperform a conventional compute node by one to two orders of magnitude. Furthermore, PIUMA continues to scale across multiple nodes, which is a challenge in conventional multi-node setups. This paper presents the PIUMA architecture, and documents our experience in designing and building a prototype chip and its bring-up process. We summarize the methodology for our co-design of the architecture together with the software stack using simulation tools and FPGA emulation. These tools provided early performance estimations of realistic applications and allowed us to implement many optimizations across the hardware, compilers, libraries and applications. We built the PIUMA chip as a 316mm2 7nm FinFET CMOS die and constructed a 16-node system. PIUMA silicon has successfully powered on demonstrating key aspects of the architecture, some of which will be incorporated into future Intel products.

cs.AR

Online and Real-time Object Tracking Algorithm with Extremely Small Matrices

Online and Real-time Object Tracking is an interesting workload that can be used to track objects (e.g., car, human, animal) in a series of video sequences in real-time. For simple object tracking on edge devices, the output of object tracking could be as simple as drawing a bounding box around a detected object and in some cases, the input matrices used in such computation are quite small (e.g., 4x7, 3x3, 5x5, etc). As a result, the amount of actual work is low. Therefore, a typical multi-threading based parallelization technique can not accelerate the tracking application; instead, a throughput based parallelization technique where each thread operates on independent video sequences is more rewarding. In this paper, we share our experience in parallelizing a Simple Online and Real-time Tracking (SORT) application on shared-memory multicores.

cs.DC