Algorithmic and architecture-oriented optimizations are essential for achieving performance worthy of anticipated energy-austere exascale systems. In this paper, we present an extreme scale FMM-accelerated boundary integral equation solver for wave scattering, which uses FMM as a matrix-vector multiplication inside the GMRES iterative method. Our FMM Helmholtz kernels are capable of treating nontrivial singular and near-field integration points. We implement highly optimized kernels for both shared and distributed memory, targeting emerging Intel extreme performance HPC architectures. We extract the potential thread-and data-level parallelism of the key Helmholtz kernels of FMM. Our application code is well optimized to exploit the AVX-512 SIMD units of Intel Skylake and Knights Landing architectures. We provide different performance models for tuning the task-based tree traversal implementation of FMM, and develop optimal architecturespecific and algorithm aware partitioning, load balancing, and communication reducing mechanisms to scale up to 6,144 compute nodes of a Cray XC40 with 196,608 hardware cores. With shared memory optimizations, we achieve roughly 77% of peak single precision floating point performance of a 56-core Skylake processor, and on average 60% of peak single precision floating point performance of a 72-core KNL. These numbers represent nearly 5.4x and 10x speedup on Skylake and KNL, respectively, compared to the the baseline scalar code. With distributed memory optimizations, on the other hand, we report near-optimal efficiency in the weak scalability study with respect to both the O(log P ) communication complexity as well as the theoretical scaling complexity of FMM. In addition, we exhibit up to 85% efficiency in strong scaling. We compute in excess of 2 billion DoF on the full-scale of the Cray XC40 supercomputer. The numerical results match the analytical solution with convergence at 1.0e-4 relative 2-norm residual accuracy. To the best of our knowledge, this work presents the fastest and the most scalable FMM-accelerated linear solver for oscillatory kernels.

Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical N -Body algorithms like FMM. In the present work, we propose four independent strategies to improve partitioning and reduce communication. First of all, we show that the conventional wisdom of using space-filling curve partitioning may not work well for boundary integral problems, which constitute about 50% of FMM's application user base. We propose an alternative method which modifies orthogonal recursive bisection to solve the cell-partition misalignment that has kept it from scaling previously. Secondly, we optimize the granularity of communication to find the optimal balance between a bulk-synchronous collective communication of the local essential tree and an RDMA per task per cell. Finally, we take the dynamic sparse data exchange proposed by Hoefler et al. [1] and extend it to a hierarchical sparse data exchange, which is demonstrated at scale to be faster than the MPI library's MPI Alltoallv that is commonly used.

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.

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We introduce AL4SAN, a lightweight library for abstracting the APIs of task-based runtime engines. AL4SAN unifies the expression of tasks and their data dependencies. It supports various dynamic runtime systems relying on compiler technology and user-defined APIs. It enables an application to employ different runtimes and their respective scheduling components, while providing user-obliviousness to the underlying hardware configurations. AL4SAN exposes common front-end APIs and connects to different back-end runtimes. Experiments on performance and overhead assessments are reported on various shared-and distributed-memory possibly hardware accelerator-equipped systems. A range of workloads, from compute-bound to memory-bound regimes, are employed as proxies for current scientific applications. The low overhead (less than 10%) achieved using a variety of workloads enables AL4SAN to be deployed for fast development of task-based numerical algorithms. More interestingly, AL4SAN enables runtime interoperability by switching runtimes at runtime. Blending runtime systems permits to achieve a twofold speedup on a task-based generalized symmetric eigenvalue solver, relative to state-of-the-art implementations. The ultimate goal of AL4SAN is not to create a new runtime, but to strengthen co-design of existing runtimes/applications, while facilitating user productivity and code portability. The code of AL4SAN is freely available at https://github.com/ecrc/al4san, with extensions in progress.

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Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native hardware. This paper attempts to conduct an elaborate performance characterization of the benefits of using quantization techniques-mainly FP16/INT8 variants with static and dynamic schemes-using the MLPerf Edge Inference benchmarking methodology. The study is conducted on Intel x86 processors and Raspberry Pi device with ARM processor. The paper uses a number of DNN inference frameworks, including OpenVINO (for Intel CPUs only), TensorFlow Lite (TFLite), ONNX, and PyTorch with MobileNetV2, VGG-19, and DenseNet-121. The single-stream, multi-stream, and offline scenarios of the MLPerf Edge Inference benchmarks are used for measuring latency and throughput in our experiments. Our evaluation reveals that OpenVINO and TFLite are the most optimized frameworks for Intel CPUs and Raspberry Pi device, respectively. We observe no loss in accuracy except for the static quantization techniques. We also observed the benefits of using quantization for these optimized frameworks. For example, INT8-based quantized models deliver 3.3× and 4× better performance over FP32 using OpenVINO on Intel CPU and TFLite on Raspberry Pi device, respectively, for the MLPerf offline scenario. To the best of our knowledge, this paper is the first one that presents a unique characterization study characterizing the impact of quantization for a range of DNN inference frameworks-including Open-VINO, TFLite, PyTorch, and ONNX-on Intel x86 processors and Raspberry Pi device with ARM processor using the MLPerf Edge Inference benchmark methodology.