A schedule management framework for aperiodic soft-real-time jobs that may be used by a CPU - GPU system designer/integrator to select, configure and deploy a suitable architectural platform and to perform concurrent scheduling of these jobs
Soft-Real-Time GP-GPU Scheduler (SRTG-Scheduler) is a dynamic scheduler for aperiodic soft-real-time jobs on GPU based architectures, with a simple, easy-to-use command-line interface (CLI). The SRTG-Scheduler is provided under the MIT license. It is currently supported on Windows, Linux, and macOS platforms.
GPUs execute at higher frequencies
- Accelerates execution of jobs allocated to it
- Improves System response time
The above image compares FLOPs per Cycle improvements over the years [1]
GPUs are energy efficient
- Power needed for GPU to carry out an operation lesser than CPUs
- Ideal for use in real time embedded system
- Significant hardware and firmware challenges
- Executions are non-preemptive
- Low degree of controllability of cores
- Policies for scheduling Real-Time jobs
- Decoding the driver
- Managing the GPU as a resource
- Targeting a Multi-GPU model
This entire body of work assumes that only one kernel may execute on a GPU at a given time(partly due to lack of hardware support)
Sending a single non-preemptive kernel on to a GPU, is under utilizing the GPU
Concurrent Kernels Execution on GPU [2]
- Safe concurrent kernels
- Performance boost
- Execution units available
Aims to develop a dynamic schedule management framework for soft-real-time jobs on GPU based architectures.
We propose to exploit this basic idea to perform coarse grained scheduling of jobs on GCUs.
Our work lays emphasis on minimal programmer involvement.
A dynamic schedule management framework that is responsible for
- Keeping track of current and expected GUC availability
- Determining which kernel(s) to dispatch to the GPU at a given time
- Determining how many GCUs to assign for a given kernel.
- GPU provides tremendous computational power under reasonable power/energy budgets
- Our work exploits concurrent kernel execution for real-time scheduling
- More economical than multi-GPU model
- Dynamic schedule management framework for soft-real-time jobs
- Support for a-periodic and recurring (periodic) soft-real-time tasks.
- Smart GPU Memory Management
note:
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