Task Based Execution of GPU Applications with Dynamic Data Dependencies Mehmet E Belviranli Chih H Chou Laxmi N Bhuyan Rajiv Gupta

GP-GPU Computing  GPUs enable high throughput data & compute intensive computations  Data is partitioned into a grid of “Thread Blocks” (TBs)  Thousands of TBs in a grid can be executed in any order  No HW support for efficient inter-TB communication  High scalability & throughput for independent data  Challenging & inefficient for inter-TB dependent data

The Problem  Data-dependent & irregular applications  Simulations (n-body, heat)  Graph algorithms (BFS, SSSP)  Inter-TB synchronization  Sync through global memory  Irregular task graphs  Static partitioning fails  Heterogeneous execution  Unbalanced distribution ! ! ! Data Dependency Graph

The Solution “Task based execution” Transition from SIMD -> MIMD

5 Challenges  Breaking applications into tasks  Task to SM assignment  Dependency tracking  Inter–SM communication  Load Balancing

6 Proposed Task Based Execution Framework  Persistent Worker TBs (per SM)  Distributed task queues (per SM)  In-GPU dependency tracking & scheduling  Load balancing via different queue insertion policies

7 Overview (1). Grab a ready Task (2). Queue (3). Retrieve & Execute (4). Output (5). Resolve Dependencies (6). Grab new

8 Concurrent Worker&Scheduler Worker Scheduler

Queue Access & Dependency Tracking  IQS and OQS  Efficient signaling mechanism via global memory  Parallel task pointer retrieval  Queues store pointers to tasks  Parallel dependency check

10 Queue Insertion Policy Round robin:  Better load balancing  Poor cache locality Tail submit: [J. Hoogerbrugge et al. ]:  First child task is always processed by the same SM with parent.  Increased locality t t+1 t+2

11 API Application specific data is added under WorkerContext and Task user_task is called by worker_kernel

12 Experimental Results  NVIDIA Tesla 2050  14 SMs, 3GB memory  Applications:  Heat 2D: Simulation of heat dissipation over a 2D surface  BFS: Breadth-first-search  Comparison:  Central queue vs. distributed queue

13 Applications Heat 2D:  Regular dependencies, wavefront parallelism.  Each tile is a task, intra-tile and inter-tile parallelism

14 Applications BFS:  Irregular dependencies.  Unreached neighbors of a node forms a task

15 Runtime

16 Scalability

17 Future Work S/W support for: Better task representation More task insertion policies Automated task graph partitioning for higher SM utilization.

18 Future Work H/W support for: Fast inter-TB sync Support for TB to SM affinity “Sleep” support for TBs

19 Conclusion  Transition from SIMD -> MIMD  Task-based execution model  Per-SM task assignment  In-GPU dependency tracking  Locality aware queue management  Room for improvement with added HW and SW support