Interactive massively parallel computations are critical for machine learning and data analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) and has required the LLSC to develop unique interactive supercomputing capabilities. Scaling interactive machine learning frameworks, such as TensorFlow, and data analysis environments, such as MATLAB/Octave, to tens of thousands of cores presents many technical challenges -in particular, rapidly dispatching many tasks through a scheduler, such as Slurm, and starting many instances of applications with thousands of dependencies. Careful tuning of launches and prepositioning of applications overcome these challenges and allow the launching of thousands of tasks in seconds on a 40,000-core supercomputer. Specifically, this work demonstrates launching 32,000 TensorFlow processes in 4 seconds and launching 262,000 Octave processes in 40 seconds. These capabilities allow researchers to rapidly explore novel machine learning architecture and data analysis algorithms.

The Spectre and Meltdown flaws in modern microprocessors represent a new class of attacks that have been difficult to mitigate. The mitigations that have been proposed have known performance impacts. The reported magnitude of these impacts varies depending on the industry sector and expected workload characteristics. In this paper, we measure the performance impact on several workloads relevant to HPC systems. We show that the impact can be significant on both synthetic and realistic workloads. We also show that the performance penalties are difficult to avoid even in dedicated systems where security is a lesser concern.

Advances in designing and training deep neural networks have led to the principle that the large and deeper a network is, the better it can perform. As a result, computational resources have become a key limiting factor in achieving better performance. One strategy to improve network capabilities while decreasing computation required is to replace dense fullyconnected and convolutional layers with sparse layers. In this paper we experiment with training on sparse neural network topologies. First, we test pruning-based sparse topologies, which use a network topology obtained by initially training a dense network and then pruning low-weight connections. Second, we test RadiX-Nets, a class of sparse network structures with proven connectivity and sparsity properties. Results show that compared to dense topologies, sparse structures show promise in training potential but also can exhibit highly nonlinear convergence, which merits further study.

Massive power-law graphs drive many fields: metagenomics, brain mapping, Internet-of-things, cybersecurity, and sparse machine learning. The development of novel algorithms and systems to process these data requires the design, generation, and validation of enormous graphs with exactly known properties. Such graphs accelerate the proper testing of new algorithms and systems and are a prerequisite for success on real applications. Many random graph generators currently exist that require realizing a graph in order to know its exact properties: number of vertices, number of edges, degree distribution, and number of triangles. Designing graphs using these random graph generators is a time-consuming trial-anderror process. This paper presents a novel approach that uses Kronecker products to allow the exact computation of graph properties prior to graph generation. In addition, when a real graph is desired, it can be generated quickly in memory on a parallel computer with no-interprocessor communication. To test this approach, graphs with 10 12 edges are generated on a 40,000+ core supercomputer in 1 second and exactly agree with those predicted by the theory. In addition, to demonstrate the extensibility of this approach, decetta-scale graphs with up to 10 30 edges are simulated in a few minutes on a laptop.