Managing GPU Concurrency in Heterogeneous Architectures
This study delves into managing GPU concurrency in heterogeneous architectures, delving into LLC memory, network, and shared resources, improving GPU and CPU performance through warp scheduler controls, CPU-centric and CPU-GPU balanced strategies. Results show positive impacts on CPU performance while mitigating latency in the GPU.
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Presentation Transcript
Managing GPU Concurrency in Heterogeneous Architectures LLC Memory Network Shared Resources
Managing GPU Concurrency in Heterogeneous Architectures LLC Memory Network Shared Resources
Managing GPU Concurrency in Heterogeneous Architectures LLC Memory Network Shared Resources
Managing GPU Concurrency in Heterogeneous Architectures LLC Memory Network Shared Resources
Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism
Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism Improved GPU performance Improved CPU performance CPU-centric Strategy
Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism Improved GPU performance Improved CPU performance CPU-centric Strategy CPU-GPU Balanced Strategy
Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism Improved GPU performance Improved CPU performance CPU-centric Strategy CPU-GPU Balanced Strategy Control the trade-off
Our Proposal CPU-centric Strategy Memory Congestion CPU Performance
Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP
Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU
Our Proposal CPU-GPU Balanced Strategy CPU-centric Strategy Memory Congestion CPU Performance GPU TLP GPU Latency Tolerance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU
Our Proposal CPU-GPU Balanced Strategy CPU-centric Strategy Memory Congestion CPU Performance GPU TLP GPU Latency Tolerance IF Memory Congestion GPU TLP IF Latency Tolerance GPU TLP Results Summary: +24% CPU & -11% GPU
Our Proposal CPU-GPU Balanced Strategy CPU-centric Strategy Memory Congestion CPU Performance GPU TLP GPU Latency Tolerance IF Memory Congestion GPU TLP IF Latency Tolerance GPU TLP Results Summary: +24% CPU & -11% GPU Results Summary: +7% both CPU & GPU
Managing GPU Concurrency in Heterogeneous Architectures Onur Kay ran1, Nachiappan CN1, Adwait Jog1, Rachata Ausavarungnirun2, Mahmut T. Kandemir1, Gabriel H. Loh3, Onur Mutlu2, Chita R. Das1 1 Penn State 2 Carnegie Mellon 3 AMD Research
Managing GPU Concurrency in Heterogeneous Architectures Onur Kay ran1, Nachiappan CN1, Adwait Jog1, Rachata Ausavarungnirun2, Mahmut T. Kandemir1, Gabriel H. Loh3, Onur Mutlu2, Chita R. Das1 1 Penn State 2 Carnegie Mellon 3 AMD Research Today Session 1B Main Auditorium @ 3 pm
