Compiler-Directed Incremental Checkpointing for Low Latency GPU Preemption

Ji, Zhuoran; Wang, Cho‐Li

doi:10.1109/ipdps53621.2022.00078

Cited by 1 publication

(2 citation statements)

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“…This request is first received by the VM's GPU driver, which places it in a waiting queue (1). As GPU requests often depend on others, these dependencies must be resolved before the request attains the "Submitted" state (2). Once all preceding requests in the queue have been executed, the guest OS's GPU driver removes the request from its waiting queue and forwards it to the vGPU for execution (3).…”

Section: Data Modelmentioning

confidence: 99%

“…Third, most GPU designs lack inherent sharing mechanisms, leading to a GPU process gaining exclusive access to its resources, thereby blocking other processes from preemption. In addition, several studies have shown that the overhead involved in process preemption is substantially higher in GPUs than in CPUs [2,3]. This increased overhead is essentially due to the higher number of cores and context states in GPUs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing GPU Performance in Virtualized Environments: A Case Study

Belkhiri,

Dagenais

2024

Future Internet

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The graphics processing unit (GPU) plays a crucial role in boosting application performance and enhancing computational tasks. Thanks to its parallel architecture and energy efficiency, the GPU has become essential in many computing scenarios. On the other hand, the advent of GPU virtualization has been a significant breakthrough, as it provides scalable and adaptable GPU resources for virtual machines. However, this technology faces challenges in debugging and analyzing the performance of GPU-accelerated applications. Most current performance tools do not support virtual GPUs (vGPUs), highlighting the need for more advanced tools. Thus, this article introduces a novel performance analysis tool that is designed for systems using vGPUs. Our tool is compatible with the Intel GVT-g virtualization solution, although its underlying principles can apply to many vGPU-based systems. Our tool uses software tracing techniques to gather detailed runtime data and generate relevant performance metrics. It also offers many synchronized graphical views, which gives practitioners deep insights into GVT-g operations and helps them identify potential performance bottlenecks in vGPU-enabled virtual machines.

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Section: Data Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%