Nvidia Corporation

Luke Durant

Lacky V Shah

Philip Alexander Cuadra

Nicholas Wang

Shirish C Gadre

Brian S Pharris

Jerome Francis Duluk, Jr

Gerald F Luiz

Robert Ohannessian

Gentaro Hirota

Arthur Merlin Danskin

Ze Long

Scott Ricketts

Jack H Choquette

John H Edmondson

Olivier Giroux

Ronny Krashinsky

Manan Patel

Alan Menezes

Jeffrey Scott Tuckey

Ravi Kiran Manyam

Jeff Tuckey

Wish Gandhi

Apoorv Parle

Prakash Bangalore Prabhakar

Steve Heinrich

Greg Palmer

Rajballav Dash

Igor Sevastiyanov

Sandeep Navada

Eric Werness

Naman Govil

Shayani Deb

Ramon Matas Navarro

Vinod Grover

Sean Jeffrey Treichler

Brian Matthew Fahs

Karim Abdalla

Timothy John Purcell

Daniel Elliot Wexler

Timothy Paul Lottes

Bastiaan Aarts

Girish Bharambe

Craig Duttweiler

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

Embodiments of the present invention set forth techniques for allocating execution resources to groups of threads within a graphics processing unit. A compute work distributor included in the graphics processing unit receives an indication from a process that a first group of threads is to be launched. The compute work distributor determines that a first subcontext associated with the process has at least one processor credit. In some embodiments, CTAs may be launched even when there are no processor credits, if one of the TPCs that was already acquired has sufficient space. The compute work distributor identifies a first processor included in a plurality of processors that has a processing load that is less than or equal to the processor loads associated with all other processors included in the plurality of processors. The compute work distributor launches the first group of threads to execute on the first processor.

Dynamic partitioning of execution resources

Techniques are provided for handling a trap encountered in a thread that is part of a thread array that is being executed in a plurality of execution units. In these techniques, a data structure with an identifier associated with the thread is updated to indicate that the trap occurred during the execution of the thread array. Also in these techniques, the execution units execute a trap handling routine that includes a context switch. The execution units perform this context switch for at least one of the execution units as part of the trap handling routine while allowing the remaining execution units to exit the trap handling routine before the context switch. One advantage of the disclosed techniques is that the trap handling routine operates efficiently in parallel processors.

Cooperative thread array granularity context switch during trap handling

A streaming multiprocessor (SM) included within a parallel processing unit (PPU) is configured to suspend a thread group executing on the SM and to save the operating state of the suspended thread group. A load-store unit (LSU) within the SM re-maps local memory associated with the thread group to a location in global memory. Subsequently, the SM may re-launch the suspended thread group. The LSU may then perform local memory access operations on behalf of the re-launched thread group with the re-mapped local memory that resides in global memory.

Technique for saving and restoring thread group operating state

Techniques are provided for restoring threads within a processing core. The techniques include, for a first thread group included in a plurality of thread groups, executing a context restore routine to restore from a memory a first portion of a context associated with the first thread group, determining whether the first thread group completed an assigned function, and, if the first thread group completed the assigned function, then exiting the context restore routine, or if the first thread group did not complete the assigned function, then executing one or more operations associated with a trap handler routine.

One embodiment of the present disclosure sets forth a technique for enforcing cross stream dependencies in a parallel processing subsystem such as a graphics processing unit. The technique involves queuing waiting events to create cross stream dependencies and signaling events to indicated completion to the waiting events. A scheduler kernel examines a task status data structure from a corresponding stream and updates dependency counts for tasks and events within the stream. When each task dependency for a waiting event is satisfied, an associated task may execute.

Method and system for processing nested stream events

A method for scheduling work for processing by a GPU is disclosed. The method includes accessing a work completion data structure and accessing a work tracking data structure. Dependency logic analysis is then performed using work completion data and work tracking data. Work items that have dependencies are then launched into the GPU by using a software work item launch interface.

System and method for runtime scheduling of GPU tasks

One embodiment of the present invention sets forth a technique for managing the allocation and release of resources during multi-threaded program execution. Programmable reference counters are initialized to values that limit the amount of resources for allocation to tasks that share the same reference counter. Resource parameters are specified for each task to define the amount of resources allocated for consumption by each array of execution threads that is launched to execute the task. The resource parameters also specify the behavior of the array for acquiring and releasing resources. Finally, during execution of each thread in the array, an exit instruction may be configured to override the release of the resources that were allocated to the array. The resources may then be retained for use by a child task that is generated during execution of a thread.

Compute work distribution reference counters

Techniques are provided for restoring thread groups in a cooperative thread array (CTA) within a processing core. Each thread group in the CTA is launched to execute a context restore routine. Each thread group, executes the context restore routine to restore from a memory a first portion of context associated with the thread group, and determines whether the thread group completed an assigned function prior to executing the context restore routine. If the thread group completed an assigned function prior to executing the context restore routine, then the thread group exits the context restore routine. If the thread group did not complete the assigned function prior to executing the context restore routine, then the thread group executes one or more operations associated with a trap handler routine. One advantage of the disclosed techniques is that the trap handling routine operates efficiently in parallel processors.

One embodiment of the present disclosure sets forth an enhanced way for GPUs to queue new computational tasks into a task metadata descriptor queue (TMDQ). Specifically, memory for context data is pre-allocated when a new TMDQ is created. A new TMDQ may be integrated with an existing TMDQ, where computational tasks within that TMDQ include task from each of the original TMDQs. A scheduling operation is executed on completion of each computational task in order to preserve sequential execution of tasks without the use of atomic locking operations. One advantage of the disclosed technique is that GPUs are enabled to queue computational tasks within TMDQs, and also create an arbitrary number of new TMDQs to any arbitrary nesting level, without intervention by the CPU. Processing efficiency is enhanced where the GPU does not wait while the CPU creates and queues tasks.

Techniques for managing the execution order of multiple nested tasks executing on a parallel processor

A system, method, and computer program product for low-latency scheduling and launch of memory defined tasks. The method includes the steps of receiving a task metadata data structure to be stored in a memory associated with a processor, transmitting the task metadata data structure to a scheduling unit of the processor, storing the task metadata data structure in a cache unit included in the scheduling unit, and copying the task metadata data structure from the cache unit to the memory.

System, method, and computer program product for low latency scheduling and launch of memory defined tasks

A system, method, and computer program product for management of dynamic task-dependency graphs. The method includes the steps of generating a first task data structure in a memory for a first task, generating a second task data structure in the memory, storing a pointer to the second task data structure in a first output dependence field of the first task data structure, setting a reference counter field of the second task data structure to a threshold value that indicates a number of dependent events associated with the second task, and launching the second task when the reference counter field stores a particular value. The second task data structure is a placeholder for a second task that is dependent on the first task.

System, method, and computer program product for management of dependency between tasks

Growing community of inventors

Santa Clara, CA, United States of America

Luke Durant