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Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory.

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Presentation on theme: "Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory."— Presentation transcript:

1 Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory

2 Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory

3 Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory

4 Managing GPU Concurrency in Heterogeneous Architect ures Shared Resources Network LLC Memory

5 Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism

6 Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism CPU-centric Strategy Improved GPU performance Improved CPU performance × 

7 Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism CPU-centric Strategy CPU-GPU Balanced Strategy Improved GPU performance Improved CPU performance ×   

8 Our Proposal Warp Scheduler Controls GPU Thread-Level Parallelism CPU-centric Strategy CPU-GPU Balanced Strategy Improved GPU performance Improved CPU performance ×    Control the trade-off

9 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance

10 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP

11 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU

12 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU CPU-GPU Balanced Strategy GPU TLP GPU Latency Tolerance

13 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU CPU-GPU Balanced Strategy GPU TLP GPU Latency Tolerance IF Latency Tolerance GPU TLP

14 Our Proposal CPU-centric Strategy Memory Congestion CPU Performance IF Memory Congestion GPU TLP Results Summary: +24% CPU & -11% GPU CPU-GPU Balanced Strategy GPU TLP GPU Latency Tolerance IF Latency Tolerance GPU TLP Results Summary: +7% both CPU & GPU

15 Managing GPU Concurrency in Heterogeneous Architect ures Onur Kayıran 1, Nachiappan CN 1, Adwait Jog 1, Rachata Ausavarungnirun 2, Mahmut T. Kandemir 1, Gabriel H. Loh 3, Onur Mutlu 2, Chita R. Das 1 1 Penn State 2 Carnegie Mellon 3 AMD Research

16 Managing GPU Concurrency in Heterogeneous Architect ures Onur Kayıran 1, Nachiappan CN 1, Adwait Jog 1, Rachata Ausavarungnirun 2, Mahmut T. Kandemir 1, Gabriel H. Loh 3, Onur Mutlu 2, Chita R. Das 1 Today Session 1B – Main Auditorium @ 3 pm 1 Penn State 2 Carnegie Mellon 3 AMD Research


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