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Papers by Fenny Kusumasari

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016

Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms a... more Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single MIC, single-node CPU/MIC collaborated parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration...

ArXiv, 2019

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable perform... more Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defences for graph data. In this paper, we propose both attack and defence techniques. For attack, we show that the discrete feature problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defence, we propose to partially learn the adjacency matrix to integrate the information of distant nodes so that the prediction of a certain target is supported by more global graph information rather than just...

ACM Transactions on the Web, 2020

Sharing a pre-trained machine learning model, particularly a deep neural network via prediction A... more Sharing a pre-trained machine learning model, particularly a deep neural network via prediction APIs, is becoming a common practice on machine learning as a service (MLaaS) platforms nowadays. Although deep neural networks (DNN) have shown remarkable successes in many tasks, they are also criticized for the lack of interpretability and transparency. Interpreting a shared DNN model faces two additional challenges compared with interpreting a general model. (1) Limited training data can be disclosed to users. (2) The internal structure of the models may not be available. These two challenges impede the application of most existing interpretability approaches, such as saliency maps or influence functions, for DNN models. Case-based reasoning methods have been used for interpreting decisions; however, how to select and organize the data points under the constraints of shared DNN models is not discussed. Moreover, simply providing cases as explanations may not be sufficient for supportin...

IEEE/ACM transactions on computational biology and bioinformatics, Jan 13, 2018

Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms a... more Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on the Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single CPU, single node CPU/MIC parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration strategy...

Mechanics and Mechanical Engineering, 2016

High Performance Computing is focusing on heterogeneous architecture. The Embarrassingly Parallel... more High Performance Computing is focusing on heterogeneous architecture. The Embarrassingly Parallel algorithm is typical of Monte Carlo method which are widely applied to many important scientific areas. In this paper, we present an efficient Hybrid Embarrassingly Parallel algorithm for heterogeneous CPU/GPU clusters and an effective task distribution model for the load balancing between CPU and GPU. Our Hybrid EP algorithm can use the computing capability of both multi-core CPU and many-core GPU simultaneously based on the task distribution model. We test Hybrid EP algorithm on various types of CPUs, GPUs and the Tianhe-1A supercomputer. The overall performance speedup of M2050 GPU ranges from 10.84 times compared with six cores X5670 to over 50.53 times compared with quad cores Q6600. The performance of heterogeneous CPU/GPU Tianhe-1A supercomputer, in which both CPU and GPU are sufficiently used, outperforms pure CPU cluster 6.86 times. The speedup increases linearly with the numbe...

2014 22nd International Conference on Pattern Recognition, 2014

Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The p... more Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.

Kanker payudara adalah kanker paling umum pada wanita baik di negara maju dan berkembang. Pengoba... more Kanker payudara adalah kanker paling umum pada wanita baik di negara maju dan berkembang. Pengobatan kanker payudara dapat dilakukan dengan cara kemoterapi. Kemoterapi menyebabkan sejumlah efek samping yang mencerminkan mekanisme kerja obat salah satunya adalah mual, salah satu yang dapat dilakukan adalah dengan pemberian aromaterapi. Tujuan penelitian ini adalah untuk mengetahui pengaruh pemberian inhalasi aromatherapy jahe terhadap tingkat mual pada pasien kanker payudara yang menjalani kemoterapi di RSPAD Gatot Soebroto. Penelitian ini menggunakan Quasi Experimental Design (Eksperimental Semu) dengan pre – post test control group. Teknik yang digunakan untuk mengumpulkan data menggunakan teknik purposive sampling sesuai dengan kriteria yang ditentukan peneliti. Sampel yang digunakan adalah pasien kanker payudara yang menjalani kemoterapi di RSPAD Gatot soebroto sebanyak 18 orang. Analisis yang digunakan adalah uji T. hasil analisis didapatkan tidak ada pengaruh yang signifikan an...

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016

Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms a... more Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single MIC, single-node CPU/MIC collaborated parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration...

ArXiv, 2019

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable perform... more Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defences for graph data. In this paper, we propose both attack and defence techniques. For attack, we show that the discrete feature problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defence, we propose to partially learn the adjacency matrix to integrate the information of distant nodes so that the prediction of a certain target is supported by more global graph information rather than just...

ACM Transactions on the Web, 2020

Sharing a pre-trained machine learning model, particularly a deep neural network via prediction A... more Sharing a pre-trained machine learning model, particularly a deep neural network via prediction APIs, is becoming a common practice on machine learning as a service (MLaaS) platforms nowadays. Although deep neural networks (DNN) have shown remarkable successes in many tasks, they are also criticized for the lack of interpretability and transparency. Interpreting a shared DNN model faces two additional challenges compared with interpreting a general model. (1) Limited training data can be disclosed to users. (2) The internal structure of the models may not be available. These two challenges impede the application of most existing interpretability approaches, such as saliency maps or influence functions, for DNN models. Case-based reasoning methods have been used for interpreting decisions; however, how to select and organize the data points under the constraints of shared DNN models is not discussed. Moreover, simply providing cases as explanations may not be sufficient for supportin...

IEEE/ACM transactions on computational biology and bioinformatics, Jan 13, 2018

Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms a... more Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on the Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single CPU, single node CPU/MIC parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration strategy...

Mechanics and Mechanical Engineering, 2016

High Performance Computing is focusing on heterogeneous architecture. The Embarrassingly Parallel... more High Performance Computing is focusing on heterogeneous architecture. The Embarrassingly Parallel algorithm is typical of Monte Carlo method which are widely applied to many important scientific areas. In this paper, we present an efficient Hybrid Embarrassingly Parallel algorithm for heterogeneous CPU/GPU clusters and an effective task distribution model for the load balancing between CPU and GPU. Our Hybrid EP algorithm can use the computing capability of both multi-core CPU and many-core GPU simultaneously based on the task distribution model. We test Hybrid EP algorithm on various types of CPUs, GPUs and the Tianhe-1A supercomputer. The overall performance speedup of M2050 GPU ranges from 10.84 times compared with six cores X5670 to over 50.53 times compared with quad cores Q6600. The performance of heterogeneous CPU/GPU Tianhe-1A supercomputer, in which both CPU and GPU are sufficiently used, outperforms pure CPU cluster 6.86 times. The speedup increases linearly with the numbe...

2014 22nd International Conference on Pattern Recognition, 2014

Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The p... more Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.

Kanker payudara adalah kanker paling umum pada wanita baik di negara maju dan berkembang. Pengoba... more Kanker payudara adalah kanker paling umum pada wanita baik di negara maju dan berkembang. Pengobatan kanker payudara dapat dilakukan dengan cara kemoterapi. Kemoterapi menyebabkan sejumlah efek samping yang mencerminkan mekanisme kerja obat salah satunya adalah mual, salah satu yang dapat dilakukan adalah dengan pemberian aromaterapi. Tujuan penelitian ini adalah untuk mengetahui pengaruh pemberian inhalasi aromatherapy jahe terhadap tingkat mual pada pasien kanker payudara yang menjalani kemoterapi di RSPAD Gatot Soebroto. Penelitian ini menggunakan Quasi Experimental Design (Eksperimental Semu) dengan pre – post test control group. Teknik yang digunakan untuk mengumpulkan data menggunakan teknik purposive sampling sesuai dengan kriteria yang ditentukan peneliti. Sampel yang digunakan adalah pasien kanker payudara yang menjalani kemoterapi di RSPAD Gatot soebroto sebanyak 18 orang. Analisis yang digunakan adalah uji T. hasil analisis didapatkan tidak ada pengaruh yang signifikan an...