Warp Scheduling Research Articles

Addressing GPU On-Chip Shared Memory Bank Conflicts Using Elastic Pipeline

One of the major problems with the GPU on-chip shared memory is bank conflicts. We analyze that the throughput of the GPU processor core is often constrained neither by the shared memory bandwidth, nor by the shared memory latency (as long as it stays constant), but is rather due to the varied latencies caused by memory bank conflicts. This results in conflicts at the writeback stage of the in-order pipeline and causes pipeline stalls, thus degrading system throughput. Based on this observation, we investigate and propose a novel Elastic Pipeline design that minimizes the negative impact of on-chip memory bank conflicts on system throughput, by decoupling bank conflicts from pipeline stalls. Simulation results show that our proposed Elastic Pipeline together with the co-designed bank-conflict aware warp scheduling reduces the pipeline stalls by up to 64.0 % (with 42.3 % on average) and improves the overall performance by up to 20.7 % (on average 13.3 %) for representative benchmarks, at trivial hardware overhead.

Exploring the limits of GPGPU scheduling in control flow bound applications

GPGPUs are optimized for graphics, for that reason the hardware is optimized for massively data parallel applications characterized by predictable memory access patterns and little control flow. For such applications' e.g., matrix multiplication, GPGPU based system can achieve very high performance. However, many general purpose data parallel applications are characterized as having intensive control flow and unpredictable memory access patterns. Optimizing the code in such problems for current hardware is often ineffective and even impractical since it exhibits low hardware utilization leading to relatively low performance. This work tracks the root causes of execution inefficacies when running control flow intensive CUDA applications on NVIDIA GPGPU hardware. We show both analytically and by simulations of various benchmarks that local thread scheduling has inherent limitations when dealing with applications that have high rate of branch divergence. To overcome those limitations we propose to use hierarchical warp scheduling and global warps reconstruction. We implement an ideal hierarchical warp scheduling mechanism we term ODGS (Oracle Dynamic Global Scheduling) designed to maximize machine utilization via global warp reconstruction. We show that in control flow bound applications that make no use of shared memory (1) there is still a substantial potential for performance improvement (2) we demonstrate, based on various synthetic and real benchmarks the feasible performance improvement. For example, MUM and BFS are parallel graph algorithms suffering from significant branch divergence. We show that in those algorithms it's possible to achieve performance gain of up to x4.4 and x2.6 relative to previously applied scheduling methods.

Non-preemptive time warp scheduling algorithms

This paper presents results of experiments we conducted on a number of scheduling algorithms used in a multi-processing Time Warp system. Our results show that system performance can be improved by using indirect indicators of Time Warp progress without going to the expense of user specified scheduling or relying on dependency graphs. Our best algorithm is based on a composite measure of simulation advance rate, flow control, and the appearance of specific message types.