Boosting Mathematical Optimization Performance and Energy Efficiency on the NVIDIA Grace CPU

This article originally appeared on developer.nvidia.com.

Mathematical optimization is a powerful tool that enables businesses and people to make smarter decisions and reach any number of goals—from improving operational efficiency to reducing costs to increasing customer satisfaction. Many of these are everyday use cases, such as scheduling a flight, pricing a hotel room, choosing a GPS route, routing delivery trucks, and more.

However, mathematical optimization is computationally intensive. Model complexity and dataset sizes require sophisticated AI algorithms and high-performance computing. As the demand for faster and better mathematical optimization solutions grows, full-stack innovation is needed from systems, software platforms, and acceleration libraries.

Founded in 2008, Gurobi is a mathematical optimization solver that solves complex problems and delivers optimal solutions within seconds to over 1,200 global customers across industries. The company received a Supermicro NVIDIA MGX-based system powered by the NVIDIA GH200 Grace Hopper Superchip, which supports fast performance at a low power consumption rate.

This blog post explores benchmark results and use cases showing improved efficiency using the Arm-based NVIDIA Grace CPU.

Setup for Mixed Integer Programming Library Computational Optimization Tests

The test platform consisted of a single NVIDIA Grace Hopper Superchip server from Supermicro, a cluster of four AMD EPYC 7313P servers, each with 16 cores and 256 GB of DDR4 memory, and Gurobi Optimizer 11.0 on Ubuntu 22.04.

The NVIDIA Grace Hopper Superchip combines an Arm-based NVIDIA Grace CPU with the NVIDIA Hopper GPU using a high-bandwidth, coherent NVIDIA NVLink-C2C (chip-to-chip) interconnect. The Grace CPU features 72 cores and 480 GB of high-performance, low-power double data rate 5x (LPDDR5X) memory.

To evaluate performance, Gurobi conducted a series of experiments using a representative benchmark set from the Mixed Integer Programming Library (MIPLIB) 2017, which contains 240 real-world optimization instances. The results for the NVIDIA Grace CPU on the Grace Hopper Superchip were compared with a cluster of AMD EPYC servers commonly used by their customers.

Preliminary Results

The first graph shows the runtime for the hard models in the MIPLIB Benchmark set.

Grace Hopper outperforms EPYC on most hard models, with an average runtime of about 80 seconds versus 130 seconds for AMD—a 38% improvement.

The following graph shows the throughput and energy for the entire MIPLIB Benchmark set. The lower the time and energy, the better the performance. Again, the NVIDIA Grace CPU outperforms AMD EPYC 7313P on both metrics, running nearly 23% faster while using 46% less energy.

Figure 3 shows the energy for the MIPLIB Benchmark set, in kWh. For each configuration, Grace Hopper consumed less energy than AMD EPYC 7313P for both thread counts, with the following results:

• At 8 threads, the NVIDIA Grace CPU uses about 1.4 kWh versus 1.75 kWh for AMD, a 20% improvement.
• At 12 threads, the NVIDIA Grace CPU uses about 1.6 kWh versus 2.6 kWh for AMD, a 38% improvement.

These results demonstrate that the Gurobi Optimizer on the NVIDIA Grace CPU achieves significant speedups and energy savings compared to AMD EPYC 7313P for solving challenging MIP models.

This is attributed to the superior multi-processing capabilities of the NVIDIA Grace CPU, which can handle the high computational and memory demands of the optimizer efficiently.

Fast, Efficient Solving with the NVIDIA Grace Hopper Superchip

Preliminary benchmarks show that Gurobi Optimizer and the NVIDIA Grace Hopper Superchip support faster computational performance with lower energy consumption, with plans to improve their results with additional tuning and testing.

This offers a promising outlook for companies across a wide range of industries that are looking to improve their energy efficiency while solving complex business challenges with better performance. For a closer look at the tests and results outlined, watch the on-demand session from NVIDIA GTC. For more insights into how mathematical optimization can help solve your most complex challenges, check out the Gurobi Resource Center.