Engineering topics

Zhang, Eddy

Publications and source records attributed to Zhang, Eddy.

3 records

DOE OSTI2016

ORION: A Framework for GPU Occupancy Tuning

An important feature of modern GPU architectures is variable occupancy. Occupancy measures the ratio between the number of active threads on a GPU and the maximum number of threads the GPU hardware can schedule. High occupancy allows a large number of threads to run simultaneously and hide memory latency, but may increase resource contention. Low occupancy has less resource contention, but is also less capable of latency hiding. Occupancy tuning is an important and challenge problem. A program running at different occupancy levels can have three to four times difference in running time. There has been limited exploration in GPU program occupancy tuning. We introduce Orion, the first GPU occupancy tuning framework.

Modeling and Performance Analysis↗

DOE OSTI2016

Tag-Split Cache for Efficient GPGPU Cache Utilization

Modern GPUs employ cache to improve memory system efficiency. However, large amount of cache space is underutilized due to irregular memory accesses and poor spatial locality which exhibited commonly in GPU applications. Our experiments show that using smaller cache lines could improve cache space utilization, but it also frequently suffers from significant performance loss by introducing large amount of extra cache requests. In this work, we propose a novel cache design named tag-split cache (TSC) that enables fine-grained cache storage to address the problem of cache space underutilization while keeping memory request number unchanged. TSC divides tag into two parts to reduce storage overhead, and it supports multiple cache line replacement in one cycle.

performance and energy optimization↗

DOE OSTI2016

New-Sum: A Novel Online ABFT Scheme For General Iterative Methods

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recover from errors when combined with a checkpoint/rollback scheme.

Instantaneous Footprint↗