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1. Coupled‐cluster singles, doubles and perturbative triples with density fitting approximation for massively parallel heterogeneous platforms

   https://doi.org/10.1002/qua.25894  

   Peng, Chong ; Calvin, Justus A. ; Valeev, Edward F. ( January 2019 , International Journal of Quantum Chemistry)

   A high‐performance implementation of the coupled‐cluster singles, doubles, and perturbative triples [CCSD(T)] is developed in the Massively Parallel Quantum Chemistry program. Novel features include: (1) reduced memory requirements via a density‐fitting (DF) CCSD implementation utilizing distributed lazy evaluation for tensors with more than two unoccupied indices and (2) the ability to utilize efficiently many‐core nodes (Intel Xeon Phi) and heterogeneous nodes with multiple NVIDIA GPUs on each node. All data that are greater than quadratic in the system size are distributed among processes. Excellent strong scaling is observed on distributed‐memory computers equipped with conventional CPUs, Intel Xeon Phi processors, and heterogeneous nodes with multiple NVIDIA GPUs Canonical CCSD(T) energies can be evaluated for systems containing 200 electrons and 1000 basis functions in a few days using a small size commodity cluster, with even larger computations possible on leadership‐class computing resources.

    

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2. HEALS: A Parallel eALS Recommendation System on CPU/GPU Heterogeneous Platforms

   https://doi.org/10.1109/HiPC53243.2021.00039  

   Wang, Qihan ; Niu, Wei ; Chen, Li ; Jin, Ruoming ; Ren, Bin ( December 2021 , 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC))

   Alternating Least Square (ALS) is a classic algorithm to solve matrix factorization widely used in recommendation systems. Existing efforts focus on parallelizing ALS on multi-/many-core platforms to handle large datasets. Recently, an optimized ALS variant called eALS was proposed, and it yields significantly lower time complexity and higher recommending accuracy than ALS. However, it is challenging to parallelize eALS on modern parallel architectures (e.g., CPUs and GPUs) because: 1) eALS’ data dependence prevents it from fine-grained parallel execution, thus eALS cannot fully utilize GPU's massive parallelism, 2) the sparsity of input data causes poor data locality and unbalanced workload, and 3) its large memory usage cannot fit into GPU's limited on-device memory, particularly for real-world large datasets. This paper proposes an efficient CPU/GPU heterogeneous recommendation system based on fast eALS for the first time (called HEALS) that consists of a set of system optimizations. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. HEALS also presents a CPU/GPU collaboration model that can explore both task parallelism and data parallelism. HEALS also optimizes this collaboration model with data communication overlapping and dynamic workload partition between CPU and GPU. Moreover, HEALS is further enhanced by various parallel techniques (e.g., loop unrolling, vectorization, and GPU parallel reduction). Evaluation results show that HEALS outperforms other state-of-the-art baselines in both performance and recommendation quality. Particularly, HEALS achieves up to 5.75 x better performance than a state-of-the-art ALS GPU library. This work also demonstrates the possibility of conducting fast recommendations on large datasets with constrained (or relaxed) hardware resources, e.g, a single CPU/GPU node. 

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3. Parallel Application Power and Performance Prediction Modeling Using Simulation

   https://doi.org/10.1109/WSC52266.2021.9715340  

   Ahmed, Kishwar ; Yoshii, Kazutomo ; Tasnim, Samia ( December 2021 , 2021 Winter Simulation Conference (WSC))

   High performance computing (HPC) system runs compute-intensive parallel applications requiring large number of nodes. An HPC system consists of heterogeneous computer architecture nodes, including CPUs, GPUs, field programmable gate arrays (FPGAs), etc. Power capping is a method to improve parallel application performance subject to variable power constraints. In this paper, we propose a parallel application power and performance prediction simulator. We present prediction model to predict application power and performance for unknown power-capping values considering heterogeneous computing architecture. We develop a job scheduling simulator based on parallel discrete-event simulation engine. The simulator includes a power and performance prediction model, as well as a resource allocation model. Based on real-life measurements and trace data, we show the applicability of our proposed prediction model and simulator. 

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4. Issues in the Reproducibility of Deep Learning Results

   https://doi.org/10.1109/SPMB47826.2019.9037840  

   Jean-Paul, S. ; Elseify, T. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, I. ; Selesnik, I. ; Picone, J. (Ed.)

   The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1]. This heterogeneous cluster uses innovative scheduling technology, Slurm [2], that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2]. We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process. Reproducible results are essential to machine learning research. Reproducibility in this context means the ability to replicate an existing experiment – performance metrics such as error rates should be identical and floating-point calculations should match closely. Three examples of ways we typically expect an experiment to be replicable are: (1) The same job run on the same processor should produce the same results each time it is run. (2) A job run on a CPU and GPU should produce identical results. (3) A job should produce comparable results if the data is presented in a different order. System optimization requires an ability to directly compare error rates for algorithms evaluated under comparable operating conditions. However, it is a difficult task to exactly reproduce the results for large, complex deep learning systems that often require more than a trillion calculations per experiment [5]. This is a fairly well-known issue and one we will explore in this poster. Researchers must be able to replicate results on a specific data set to establish the integrity of an implementation. They can then use that implementation as a baseline for comparison purposes. A lack of reproducibility makes it very difficult to debug algorithms and validate changes to the system. Equally important, since many results in deep learning research are dependent on the order in which the system is exposed to the data, the specific processors used, and even the order in which those processors are accessed, it becomes a challenging problem to compare two algorithms since each system must be individually optimized for a specific data set or processor. This is extremely time-consuming for algorithm research in which a single run often taxes a computing environment to its limits. Well-known techniques such as cross-validation [5,6] can be used to mitigate these effects, but this is also computationally expensive. These issues are further compounded by the fact that most deep learning algorithms are susceptible to the way computational noise propagates through the system. GPUs are particularly notorious for this because, in a clustered environment, it becomes more difficult to control which processors are used at various points in time. Another equally frustrating issue is that upgrades to the deep learning package, such as the transition from TensorFlow v1.9 to v1.13, can also result in large fluctuations in error rates when re-running the same experiment. Since TensorFlow is constantly updating functions to support GPU use, maintaining an historical archive of experimental results that can be used to calibrate algorithm research is quite a challenge. This makes it very difficult to optimize the system or select the best configurations. The overall impact of all of these issues described above is significant as error rates can fluctuate by as much as 25% due to these types of computational issues. Cross-validation is one technique used to mitigate this, but that is expensive since you need to do multiple runs over the data, which further taxes a computing infrastructure already running at max capacity. GPUs are preferred when training a large network since these systems train at least two orders of magnitude faster than CPUs [7]. Large-scale experiments are simply not feasible without using GPUs. However, there is a tradeoff to gain this performance. Since all our GPUs use the NVIDIA CUDA® Deep Neural Network library (cuDNN) [8], a GPU-accelerated library of primitives for deep neural networks, it adds an element of randomness into the experiment. When a GPU is used to train a network in TensorFlow, it automatically searches for a cuDNN implementation. NVIDIA’s cuDNN implementation provides algorithms that increase the performance and help the model train quicker, but they are non-deterministic algorithms [9,10]. Since our networks have many complex layers, there is no easy way to avoid this randomness. Instead of comparing each epoch, we compare the average performance of the experiment because it gives us a hint of how our model is performing per experiment, and if the changes we make are efficient. In this poster, we will discuss a variety of issues related to reproducibility and introduce ways we mitigate these effects. For example, TensorFlow uses a random number generator (RNG) which is not seeded by default. TensorFlow determines the initialization point and how certain functions execute using the RNG. The solution for this is seeding all the necessary components before training the model. This forces TensorFlow to use the same initialization point and sets how certain layers work (e.g., dropout layers). However, seeding all the RNGs will not guarantee a controlled experiment. Other variables can affect the outcome of the experiment such as training using GPUs, allowing multi-threading on CPUs, using certain layers, etc. To mitigate our problems with reproducibility, we first make sure that the data is processed in the same order during training. Therefore, we save the data from the last experiment and to make sure the newer experiment follows the same order. If we allow the data to be shuffled, it can affect the performance due to how the model was exposed to the data. We also specify the float data type to be 32-bit since Python defaults to 64-bit. We try to avoid using 64-bit precision because the numbers produced by a GPU can vary significantly depending on the GPU architecture [11-13]. Controlling precision somewhat reduces differences due to computational noise even though technically it increases the amount of computational noise. We are currently developing more advanced techniques for preserving the efficiency of our training process while also maintaining the ability to reproduce models. In our poster presentation we will demonstrate these issues using some novel visualization tools, present several examples of the extent to which these issues influence research results on electroencephalography (EEG) and digital pathology experiments and introduce new ways to manage such computational issues. 

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5. Using HPX and OP2 for Improving Parallel Scaling Performance of Unstructured Grid Applications

   https://doi.org/10.1109/ICPPW.2016.39  

   Khatami, Zahra ; Kaiser, Hartmut ; Ramanujam, J. ( August 2016 , 2016 45th International Conference on Parallel Processing Workshops (ICPPW))

   Computer scientists and programmers face the difficultly of improving the scalability of their applications while using conventional programming techniques only. As a base-line hypothesis of this paper we assume that an advanced runtime system can be used to take full advantage of the available parallel resources of a machine in order to achieve the highest parallelism possible. In this paper we present the capabilities of HPX - a distributed runtime system for parallel applications of any scale - to achieve the best possible scalability through asynchronous task execution [1]. OP2 is an active library which provides a framework for the parallel execution for unstructured grid applications on different multi-core/many-core hardware architectures [2]. OP2 generates code which uses OpenMP for loop parallelization within an application code for both single-threaded and multi-threaded machines. In this work we modify the OP2 code generator to target HPX instead of OpenMP, i.e. port the parallel simulation backend of OP2 to utilize HPX. We compare the performance results of the different parallelization methods using HPX and OpenMP for loop parallelization within the Airfoil application. The results of strong scaling and weak scaling tests for the Airfoil application on one node with up to 32 threads are presented. Using HPX for parallelization of OP2 gives an improvement in performance by 5%-21%. By modifying the OP2 code generator to use HPX's parallel algorithms, we observe scaling improvements by about 5% as compared to OpenMP. To fully exploit the potential of HPX, we adapted the OP2 API to expose a future and dataflow based programming model and applied this technique for parallelizing the same Airfoil application. We show that the dataflow oriented programming model, which automatically creates an execution tree representing the algorithmic data dependencies of our application, improves the overall scaling results by about 21% compared to OpenMP. Our results show the advantage of using the asynchronous programming model implemented by HPX. 

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