Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps
CoRR abs/1602.01348 (2016), arXiv version.
Nandita Vijaykumar, Gennady Pekhimenko, Adwait Jog†, Saugata Ghose,
Abhishek Bhowmick, Rachata Ausavarungnirun, Chita R. Das†,
Mahmut T. Kandemir†, Todd C. Mowry, Onur Mutlu
Carnegie Mellon University
† Pennsylvania State University
Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the cores. For example, when a GPU is bottlenecked by the available off-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive.
This work describes the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides Wexible mechanisms to automatically generate “assist warps” that execute on GPU cores to perform specific tasks that can improve GPU performance and efficiency.
CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps.
We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.
We believe that CABA is a flexible framework that enables the use of idle resources to improve application performance with diUerent optimizations and perform other useful tasks. We discuss how CABA can be used, for example, for memoization, prefetching, handling interrupts, profiling, redundant multithreading, and speculative precomputation.
FULL PAPER: pdf