Joe Eaton holds a Ph.D. in Computational and Applied Mathematics from the University of Texas at Austin's TICAM program. His fascination with CFD and fluid mechanics led to two Mechanical Engineering degrees (Rice University and Stanford University) before he decided it was really all about the math. Joe's Ph.D. work was on AMG applied to reservoir simulation problems, mixed with high performance chemistry simulation and parallel computing. He joined NVIDIA in 2013 to lead the AmgX product team.
[Note: Lung Sheng Chien from NVIDIA also contributed to this post.]
A key bottleneck for most science and engineering simulations is the solution of sparse linear systems of equations, which can account for up to 95% of total simulation time. There are two types of solvers for these systems: iterative and direct solvers. Iterative solvers are favored for the largest systems these days (see my earlier posts about AmgX), while direct solvers are useful for smaller systems because of their accuracy and robustness.
CUDA 7 expands the capabilities of GPU-accelerated numerical computing with cuSOLVER, a powerful new suite of direct linear system solvers. These solvers provide highly accurate and robust solutions for smaller systems, and cuSOLVER offers a way of combining many small systems into a ‘batch’ and solving all of them in parallel, which is critical for the most complex simulations today. Combustion models, bio-chemical models and advanced high-order finite-element models all benefit directly from this new capability. Computer vision and object detection applications need to solve many least-squares problems, so they will also benefit from cuSOLVER.
Direct solvers rely on algebraic factorization of a matrix, which breaks a hard-to-solve matrix into two or more easy-to-solve factors, and a solver routine which uses the factors and a right hand side vector and solves them one at a time to give a highly accurate solution. Figure 1 shows an example of factorization of a dense matrix. A solver for this factorization would first solve the transpose of L part, then apply the inverse of the D (diagonal) part in parallel, then solve again with L to arrive at the final answer. The benefit of direct solvers is that (unlike iterative solvers), they always find a solution (when the factors exist; more on this later) and once a factorization is found, solutions for many right-hand sides can be performed using the factors at a much lower cost per solution. Also, for small systems, direct solvers are typically faster than iterative methods because they only pass over the matrix once.
In this post I give an overview of cuSOLVER followed by an example of using batch QR factorization for solving many sparse systems in parallel. In a followup post I will cover other aspects of cuSOLVER, including dense system solvers and the cuSOLVER refactorization API.
Back in January I wrote a post about the public beta availability of AmgX, a linear solver library for large-scale industrial applications. Since then, AmgX has grown up! Now we can solve problems that were impossible for us before, due to the addition of “classical” Algebraic Multi-Grid (often called Ruge-Stueben AMG). V1.0 comes complete with classical AMG multi-GPU support, greatly improved scalability, and we have some nice performance numbers to back it up.
Models of Flow
One specific class of problem has eluded us, until now. In the oil and gas industry, reservoir simulation is used to predict the behavior of wells producing from large hydrocarbon deposits, and more recently from shale gas or shale oil fields. These problems are models of flow through porous media, coupled with flow through networks of fractures, piping and processing equipment, but it is the media that makes all the difference. Oil and gas deposits aren’t like big caves with lakes of oil, they are more like complex, many-layered sponges, each with different pore sizes, stiffness and hydrocarbon content.
Many industries use Computational Fluid Dynamics (CFD) to predict fluid flow forces on products during the design phase, using only numerical methods. A famous example is Boeing’s 777 airliner, which was designed and built without the construction (or destruction) of a single model in a wind tunnel, an industry first. This approach dramatically reduces the cost of designing new products for which aerodynamics is a large part of the value add. Another good example is Formula 1 racing, where a fraction of a percentage point reduction in drag forces on the car body can make the difference between a winning or a losing season.
Users of CFD models crave higher accuracy and faster run times. The key enabling algorithm for realistic models in CFD is Algebraic Multi-Grid (AMG). This algorithm allows solution times to scale linearly with the number of unknowns in the model; it can be applied to arbitrary geometries with highly refined and unstructured numerical meshes; and it can be run efficiently in parallel. Unfortunately, AMG is also very complex and requires specialty programming and mathematical skills, which are in short supply. Add in the need for GPU programming skills, and GPU-accelerated AMG seems a high mountain to climb. Existing GPU-accelerated AMG implementations (most notably the one in CUSP) are more proofs of concept than industrial strength solvers for real world CFD applications, and highly tuned multi-threaded and/or distributed CPU implementations can outperform them in many cases. Industrial CFD users had few options for GPU acceleration, so NVIDIA decided to do something about it.
NVIDIA partnered with ANSYS, provider of the leading CFD software Fluent to develop a high-performance, robust and scalable GPU-accelerated AMG library. We call the library AmgX (for AMG Accelerated). Fluent 15.0 uses AmgX as its default linear solver, and it takes advantage of a CUDA-enabled GPU when it detects one. AmgX can even use MPI to connect clusters of servers to solve very large problems that require dozens of GPUs. The aerodynamics problem in Figure 1 required 48 NVIDIA K40X GPUs, and involved 111million cells and over 440 million unknowns. Continue reading →