HiPEAC futures
Sim through transatlantic collaboration Korea Advanced Institute of Science and Technology-KAIST), AMD Corporation, and myself from Murcia, Spain (UCAM). Thanks to this joint effort, last year our team built a novel multiGPU simulator called MGPUSim that was recently accepted for publication at ISCA 2019, and we are preparing a tutorial on this simulator for HPCA 2020 that will be held in San Diego, CA in February 2020. I am very fortunate as I maintain an active collaboration with the team via regular weekly meetings over Slack. Undoubtedly, this is providing me with new opportunities in my career and has enhanced my skills as a professor and computer architect. In March 2017 I became a full member of HiPEAC, and at the
David Kaeli’s research group
beginning of this year I was promoted to associate professor.
Creating MGPUSim, a multi-GPU simulator
support simulation of state-of-the-art multi-GPU platforms. This is mainly because: 1) existing GPU simulators simulate dated GPU
A multi-GPU system is a promising energy-efficient computing
architectures and cannot easily model multi-GPU communication
platform that can provide the required computing power
features; 2) existing simulators lack modularity and extensibility,
demanded by today’s large-scale data-driven applications. As a
making modelling and configuring a multi-GPU platform a
result, both industry and academia are looking for better multi-
tedious task; and 3) existing simulators are not efficient in terms
GPU solutions. For example, NVIDIA ships DGX-1 and DGX-2
of simulation speed. So researchers are disadvantaged when
systems, integrating up to 16 GPUs in each node. The systems are
studying multi-GPU systems.
targeted primarily at deep neural network workloads. Similarly, AMD integrates four MI25 GPUs in its TS4 servers to accelerate
To address this, we created MGPUSim, as described in HiPEACinfo
deep learning applications.
58. We validated MGPUSim against real hardware with an error as low as 5.5% when compared with real GPU execution. The value
However, the computer architecture research community does
of MGPUSim is not limited to only multi-GPU system simulation,
not have an open source, flexible, high-performance and reliable
but can be used to drive studies on state-of-the-art single-GPU
multi-GPU simulator. Current publicly-available GPU simulators
performance. We have released MGPUSim as an open-source tool
were originally developed for single GPU platforms and cannot
under the MIT License, so researchers from both academia and industry can take advantage of a flexible, high-performance and reliable simulator for their research. Our plan is to continue our fruitful collaboration and apply for funding under the umbrella of designing optimized multiGPU systems. Professor Kaeli and Professor Joshi have recently submitted an NSF Computer and Network Systems four-year project proposal where, if granted, I will be participating as an external collaborator. We will also look for other calls in Europe.
Ajay Joshi’s research group
HiPEACINFO 59 33