Network congestion is one of the biggest problems facing HPC systems today, affecting system throughput, performance, user experience, and reproducibility. Congestion manifests as run-to-run variability due to contention for shared resources (e.g., filesystems) or routes between compute endpoints. Despite its significance, current network benchmarks fail to proxy the real-world network utilization seen on congested systems. We propose a new open-source benchmark suite called the Global Performance and Congestion Network Tests (GPCNeT) to advance the state of the practice in this area. The guiding principles used in designing GPCNeT are described and the methodology employed to maximize its utility is presented. The capabilities of GPCNeT are evaluated by analyzing results from several world's largest HPC systems, including an evaluation of congestion management on a next-generation network. The results show that systems of all technologies and scales are susceptible to congestion and this work motivates the need for congestion control in next-generation networks.

Summary Hermitian (symmetric) eigenvalue solvers are the core constituents of electronic structure, quantum‐chemistry, and other HPC applications such as Quantum ESPRESSO, VASP, CP2K, and NWChem to name a few. Our understanding of the performance of symmetric eigenvalue algorithms on various hardware is clearly important to the quantum chemistry or condensed matter physics community but in fact goes beyond that community. For instance, big data analytics is increasingly utilizing eigenvalues solvers, in the study of randomized singular value decomposition (SVD) or principal component analysis (PCA). Noise, vibration, and harshness (NVH) is another field where fast and efficient eigenvalue solvers are required. Most eigenvalue solver packages feature numerous different parameters which can be tuned for performance, eg, the number of nodes, number of total ranks, the decomposition of the matrix, etc. In this paper, we investigate the performance of different packages as well as the influence of these knobs on the solver performance.

We develop a method for improving the parallel scalability of computations that involve asynchronous task execution. We apply this method to the recently developed parallel selected inversion algorithm [Jacquelin, Lin and Yang 2014], named PSelInv, on massively parallel distributed memory machines. In the PSelInv method, we compute selected elements of the inverse of a sparse matrix A that can be decomposed as A = LU, where L is lower triangular and U is upper triangular. Computing these selected elements of A-1 requires restricted collective communications among a subset of processors within each column or row communication group created by a block cyclic distribution of L and U. We describe how this type of restricted collective communication can be implemented using asynchronous point-to-point MPI communications combined with a binary tree based data propagation scheme. Because multiple restricted collective communications may take place at the same time, we need to use a heuristic to prevent processors participating in multiple collective communications from receiving too many messages. This heuristic allows us to reduce communication load imbalance and improve the overall scalability of the selected inversion algorithm. For instance, when 6, 400 processors are used, we observe that the use of this heuristic leads to over 5x speedup for a number of test matrices. It also mitigates the performance variability introduced by an inhomogeneous network topology.

We present novel parallel language constructs for the communication intensive part of a magnetic fusion simulation code. The focus of this work is the shift phase of charged particles of a tokamak simulation code in toroidal geometry. We introduce new hybrid PGAS/OpenMP implementations of highly optimized hybrid MPI/OpenMP based communication kernels. The hybrid PGAS implementations use an extension of standard hybrid programming techniques, enabling the distribution of high communication work loads of the underlying kernel among OpenMP threads. Building upon lightweight one-sided CAF (Fortran 2008) communication techniques, we also show the benefits of spreading out the communication over a longer period of time, resulting in a reduction of bandwidth requirements and a more sustained communication and computation overlap. Experiments on up to 130560 processors are conducted on the NERSC Hopper system, which is currently the largest HPC platform with hardware support for one-sided communication and show performance improvements of 52 % at highest concurrency.