During HPL execution on a DGX cluster, the benchmark fails with "not enough memory" errors despite sufficient physical RAM. Which HPL.dat parameter…

The High-Performance Linpack (HPL) benchmark's memory consumption is dominated by the storage of the matrix A, which requires N² sizeof(double) bytes. When HPL fails with "not enough memory" errors, it means the problem size (N) is too large for the aggregate physical RAM of the cluster. The most direct and effective way to resolve this is to reduce the value of N in the HPL.dat configuration file. This directly decreases the total memory footprint of the problem, allowing it to fit within the available system memory. While other parameters affect performance, N is the primary driver of memory usage.

The user did not provide the incorrect options. Hypothetical incorrect options and explanations are provided below.

B) Increase the block size (NB): Increasing NB primarily affects cache usage and performance. It has a minor impact on workspace memory but does not reduce the main N² matrix size.

C) Change the process grid (P & Q): Modifying the process grid redistributes the existing large problem across nodes but does not reduce the total memory required to store the entire matrix.

D) Increase the number of GPUs used: Adding more GPUs will not help with a system RAM (host memory) exhaustion issue, as the main HPL matrix resides in CPU memory, not GPU memory.

1. Netlib Repository, HPL Documentation. "Tuning HPL." Section: "Problem Size (N)". The official HPL documentation states, "The amount of memory used by HPL is almost entirely determined by the size of the matrix, N." It guides users to select N based on available memory.

2. Dongarra, J., Luszczek, P., & Petitet, A. (2003). "The LINPACK Benchmark: Past, Present, and Future." Concurrency and Computation: Practice and Experience, 15(9), 803-820. (https://doi.org/10.1002/cpe.728). This paper describes the benchmark's methodology, implicitly linking the matrix size N directly to memory requirements. Section 3 discusses the algorithm's memory complexity.