Blogs (2) >>
ISMM 2017
Sun 18 Jun 2017 Barcelona, Spain
co-located with PLDI 2017
Sun 18 Jun 2017 14:00 - 14:30 at Aula Master - Session 3: Hybrid Memory Systems Chair(s): Ben L. Titzer

Heterogeneous systems that integrate a multicore CPU and a GPU on the same die are ubiquitous. On these systems, both the CPU and GPU share the same physical memory as opposed to using separate memory dies. Although integration eliminates the need to copy data between the CPU and the GPU, arranging transparent memory sharing between the two devices can carry large overheads. Memory on CPU/GPU systems is typically managed by a software framework such as OpenCL or CUDA, a runtime library, and a GPU driver. These frameworks offer a range of memory management methods that vary in ease of use, consistency guarantees and performance. In this study, we analyze some of the common memory management methods of the most widely used software frameworks for heterogeneous systems: CUDA, OpenCL 1.2, OpenCL 2.0, and HSA, on NVIDIA and AMD hardware. We focus on performance/functionality trade-offs, with the goal of exposing their performance impact and simplifying the choice of memory management methods for programmers.

Sun 18 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:00 - 15:30
Session 3: Hybrid Memory SystemsISMM 2017 at Aula Master
Chair(s): Ben L. Titzer Google
14:00
30m
Talk
Analyzing Memory Management Methods on Integrated CPU-GPU Systems
ISMM 2017
Mohammad Dashti University of British Columbia, Alexandra (Sasha) Fedorova Simon Fraser University
14:30
30m
Talk
Continuous Checkpointing of HTM Transactions in NVM
ISMM 2017
Ellis Giles Rice University, Kshitij Doshi Intel Corporation, Peter Varman Rice University
15:00
30m
Talk
RTHMS: A Tool for Data Placement on Hybrid Memory System
ISMM 2017
Ivy Bo Peng KTH Royal Institute of Technology, Roberto Gioiosa Pacific Northwest National Laboratory, Gokcen Kestor Pacific Northwest National Laboratory, Stefano Markidis KTH Royal Institute of Technology, Pietro Cicotti San Diego Supercomputer Center, Erwin Laure KTH Royal Institute of Technology