Nvidia Corporation

Shayani Deb

Jerome Francis Duluk, Jr

Jack H Choquette

Shirish C Gadre

Lacky V Shah

John H Edmondson

Olivier Giroux

Ronny Krashinsky

Manan Patel

Luke Durant

Gentaro Hirota

Brian S Pharris

Ze Long

Ravi Kiran Manyam

Jeff Tuckey

Prakash Bangalore Prabhakar

Apoorv Parle

Wish Gandhi

Rajballav Dash

Steve Heinrich

Greg Palmer

Sandeep Navada

Eric Werness

Naman Govil

A new level(s) of hierarchy—Cooperate Group Arrays (CGAs)—and an associated new hardware-based work distribution/execution model is described. A CGA is a grid of thread blocks (also referred to as cooperative thread arrays (CTAs)). CGAs provide co-scheduling, e.g., control over where CTAs are placed/executed in a processor (such as a GPU), relative to the memory required by an application and relative to each other. Hardware support for such CGAs guarantees concurrency and enables applications to see more data locality, reduced latency, and better synchronization between all the threads in tightly cooperating collections of CTAs programmably distributed across different (e.g., hierarchical) hardware domains or partitions.

Cooperative group arrays

Distributed shared memory (DSMEM) comprises blocks of memory that are distributed or scattered across a processor (such as a GPU). Threads executing on a processing core local to one memory block are able to access a memory block local to a different processing core. In one embodiment, shared access to these DSMEM allocations distributed across a collection of processing cores is implemented by communications between the processing cores. Such distributed shared memory provides very low latency memory access for processing cores located in proximity to the memory blocks, and also provides a way for more distant processing cores to also access the memory blocks in a manner and using interconnects that do not interfere with the processing cores' access to main or global memory such as hacked by an L2 cache. Such distributed shared memory supports cooperative parallelism and strong scaling across multiple processing cores by permitting data sharing and communications previously possible only within the same processing core.

Distributed shared memory

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Seattle, WA, United States of America

Shayani Deb