TRIPP: Transparent Resource Provisioning for Multi-Tenant CPU-GPU based Cloud Environments

• Julio Costella Vicenzi UFRGS
• Tiago Knorst UFRGS
• Michael G. Jordan UFRGS
• Guilherme Korol UFRGS
• Antonio Carlos Schneider Beck UFRGS
• Mateus Beck Rutzig UFSM

Resumo

Cloud Warehouses have been expanding their computational resources to cover the growing offloading of tenants’ applications. Currently, cloud nodes integrate heterogeneous resources, such as CPU and GPU, so they can exploit different types and levels of parallelism available in the applications. However, heterogeneous cloud nodes bring challenges to the software development process, since the programmer must be aware of each device’s specifications, analyze and distribute the code over the available devices. Even though OpenCL supports transparent programming on heterogeneous devices, softening the programmer’s burden, the choice of target device is still the programmer’s responsibility. Given that, this work proposes a framework for the execution of OpenCL applications on a multi-tenant CPU-GPU cloud environment, responsible for transparently scheduling the applications to the best available device, without any interaction from the programmer. The framework has the goal of optimizing resource provisioning, reducing makespan and energy consumption. Considering the execution of the PolyBench benchmark suite, the framework shows reduction on makespan of $3.4 \times$ and energy savings of 33% when compared to the GPU standalone execution.

Palavras-chave: Runtime, Scheduling algorithms, Graphics processing units, Programming, Parallel processing, Systems engineering and theory, Software, OpenCL, Cloud computing, Heterogeneous Systems, Parallel programming