It’s clear co-design is a vital component among activities required to achieve exascale computing. The leadership and early directions of the four co-design centers so far established in support of DOE’s Exascale Computing Project were summarized late last week in an article posted on the Argonne National Laboratory web site.
The four centers created include:
- Co-design Center for Online Data Analysis and Reduction at the Exascale (CODAR)
- Center for Efficient Exascale Discretizations (CEED)
- Co-design Center for Particle Applications (CoPA)
- Block-Structured Adaptive Mesh Refinement Co-design Center (BSAMR)
The term ’co-design’ describes the integrated development and evolution of hardware technologies, computational applications and associated software.” In pursuit of ECP’s mission to help people solve realistic application problems through exascale computing, each co-design center targets different features and challenges relating to exascale computing.” The full article, Co-design centers to help make next-generation exascale computing a reality, written by Joan Koka, identifies the leaders and briefly touches on goals for each center.
Ian Foster, a University of Chicago professor and Argonne Distinguished Fellow, leads the CODAR effort, “Exascale systems will be 50 times faster than existing systems, but it would be too expensive to build out storage that would be 50 times faster as well,” he said. “This means we no longer have the option to write out more data and store all of it. And if we can’t change that, then something else needs to change.”
There are many powerful techniques for doing data reduction, and CODAR researchers are studying various approaches. One example is lossy compression which attempts to remove unnecessary or redundant information to reduce overall data size. This technique is what’s used to transform the detail-rich images captured on our phone camera sensors into JPEG files, which are small in size. While data is lost in the process, the most important information ― the amount needed for our eyes to interpret the images clearly ― is maintained, and as a result, we can store hundreds more photos on our devices.
CEED is looking at the process of discretization in which the physics of the problem is represented as a finite number of grid points that represent the model of the system. “Determining the best layout of the grid points and representation of the model is important for rapid simulation,” said computational scientist Misun Min, the Argonne lead in CEED.
Discretization enables researchers to numerically represent physical systems, like nuclear reactors, combustion engines or climate systems. How researchers discretize the systems they’re studying affects the amount and speed of computation at exascale. CEED is focused particularly on high-order discretizations that require relatively few grid points to accurately represent physical systems.
Researchers are studying methods that model natural phenomena using particles, such as molecules, electrons or atoms. Particle methods span a wide range of application areas, including materials science, chemistry, cosmology, molecular dynamics and turbulent flows. When using particle methods, researchers characterize the interactions of particles with other particles and with their environment in terms of short-range and long-range interactions.
“The idea behind the co-design center is that, instead of everyone bringing their own specialized methods, we identify a set of building blocks, and then find the right way to deal with the common problems associated with these methods on the new supercomputers,” Salman Habib, the Argonne lead in CoPA and a senior member of the Kavli Institute for Cosmological Physics at the University of Chicago, said.
AMR allows an application to achieve higher level of precision at specific points or locations of interest within the computational domain and lower levels of precision elsewhere. “Without AMR, calculations would require so much more resources and time,” said Anshu Dubey, the Argonne lead in the Block-Structured AMR Center and a fellow of the Computation Institute. AMR is already used in applications such as combustion, astrophysics and cosmology; now researchers in the Block-Structured AMR co-design center are focused on enhancing and augmenting it for future exascale platforms.