ICPP'18 paper :
"Parallelizing Pruning-based Graph Structural Clustering"
For details (codes/experimental results), see
https://github.com/GraphProcessor/ppSCAN
Concurrent Replication of Parallel and Distributed SimulationsGabriele D'Angelo
Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation.
Controlled dropout: a different dropout for improving training speed on deep ...Byung Soo Ko
"Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Load balancing in public cloud combining the concepts of data mining and netw...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
(Paper) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Concurrent Replication of Parallel and Distributed SimulationsGabriele D'Angelo
Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation.
Controlled dropout: a different dropout for improving training speed on deep ...Byung Soo Ko
"Controlled Dropout" is a different dropout method which is for improving training speed on deep neural networks. Basic idea and algorithm of controlled dropout are based on the paper "Controlled Dropout: a Different Dropout for Improving Training Speed on Deep Neural Network" which was presented in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Load balancing in public cloud combining the concepts of data mining and netw...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
(Paper) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Analysis of Multi Level Feedback Queue Scheduling Using Markov Chain Model wi...Eswar Publications
When a process gets the CPU, the scheduler has no idea of the precise amount of CPU time the process will need.
Process scheduling algorithms are used for better utilization of CPU. The number of processes arriving to the CPU at a time comes in mass volume which causes a long waiting queue. In Multilevel feedback queue scheduling, the scheduler moves from one queue to another in order to perform the processing follow the transition mechanism. This paper analysed a general transition scenario for the functioning of CPU scheduler in multilevel queue with feedback mechanism. We proposed a Markov chain model to analyze this transition phenomenon
with a general class of scheduling scheme. Simulation study is performed to evaluate the comparative study with the help of varying values of α and d in a mathematical model.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Grid computing is a modularized way of structuring network resources so as to share the information and resources, to perform heavily intense problems. Grid computing is a part of distributed processing which allows the distribution of the problem over multiple computational resources. The formation of grid decides the computation cost, the performance metrics of the operation to be carried out. The grid computing favours the formation of an adhoc structure formed at the time of request (it does not hold an infrastructure) that is termed as virtual organization. This paper presents dynamic source routing for modeling virtual organization.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Burton Smith passed away on April 3, 2018. He gave this talk in 2014.
"This talk proposes a scheme for addressing the operating system resource management problem. Resource management is the dynamic allocation and de-allocation by an operating system of processor cores, memory pages, and various types of bandwidth to computations that compete for those resources. The objective is to allocate resources for optimized responsiveness based on the finite resources available."
Read the full story: https://wp.me/p3RLHQ-ihD
Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost an...Naoki Shibata
Yosuke Wakisaka, Naoki Shibata, Keiichi Yasumoto, Minoru Ito, and Junji Kitamichi : Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost and Hyper-Threading, In Proc. of The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications(PDPTA'14), pp. 229-235
In this paper, we propose a task scheduling algorithm for multiprocessor systems with Turbo Boost and Hyper-Threading technologies. The proposed algorithm minimizes the total computation time taking account of dynamic changes of the processing speed by the two technologies, in addition to the network contention among the processors. We constructed a clock speed model with which the changes of processing speed with Turbo Boost and Hyper-threading can be estimated for various processor usage patterns. We then constructed a new scheduling algorithm that minimizes the total execution time of a task graph considering network contention and the two technologies. We evaluated the proposed algorithm by simulations and experiments with a multiprocessor system consisting of 4 PCs. In the experiment, the proposed algorithm produced a schedule that reduces the total execution time by 36% compared to conventional methods which are straightforward extensions of an existing method.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Lecture 4 principles of parallel algorithm design updatedVajira Thambawita
The main principles of parallel algorithm design are discussed here. For more information: visit, https://sites.google.com/view/vajira-thambawita/leaning-materials
The method of identifying similar groups of data in a data set is called clustering. Entities in each group are comparatively more similar to entities of that group than those of the other groups.
On selection of periodic kernels parameters in time series predictioncsandit
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
Analysis of Multi Level Feedback Queue Scheduling Using Markov Chain Model wi...Eswar Publications
When a process gets the CPU, the scheduler has no idea of the precise amount of CPU time the process will need.
Process scheduling algorithms are used for better utilization of CPU. The number of processes arriving to the CPU at a time comes in mass volume which causes a long waiting queue. In Multilevel feedback queue scheduling, the scheduler moves from one queue to another in order to perform the processing follow the transition mechanism. This paper analysed a general transition scenario for the functioning of CPU scheduler in multilevel queue with feedback mechanism. We proposed a Markov chain model to analyze this transition phenomenon
with a general class of scheduling scheme. Simulation study is performed to evaluate the comparative study with the help of varying values of α and d in a mathematical model.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Grid computing is a modularized way of structuring network resources so as to share the information and resources, to perform heavily intense problems. Grid computing is a part of distributed processing which allows the distribution of the problem over multiple computational resources. The formation of grid decides the computation cost, the performance metrics of the operation to be carried out. The grid computing favours the formation of an adhoc structure formed at the time of request (it does not hold an infrastructure) that is termed as virtual organization. This paper presents dynamic source routing for modeling virtual organization.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Burton Smith passed away on April 3, 2018. He gave this talk in 2014.
"This talk proposes a scheme for addressing the operating system resource management problem. Resource management is the dynamic allocation and de-allocation by an operating system of processor cores, memory pages, and various types of bandwidth to computations that compete for those resources. The objective is to allocate resources for optimized responsiveness based on the finite resources available."
Read the full story: https://wp.me/p3RLHQ-ihD
Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost an...Naoki Shibata
Yosuke Wakisaka, Naoki Shibata, Keiichi Yasumoto, Minoru Ito, and Junji Kitamichi : Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost and Hyper-Threading, In Proc. of The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications(PDPTA'14), pp. 229-235
In this paper, we propose a task scheduling algorithm for multiprocessor systems with Turbo Boost and Hyper-Threading technologies. The proposed algorithm minimizes the total computation time taking account of dynamic changes of the processing speed by the two technologies, in addition to the network contention among the processors. We constructed a clock speed model with which the changes of processing speed with Turbo Boost and Hyper-threading can be estimated for various processor usage patterns. We then constructed a new scheduling algorithm that minimizes the total execution time of a task graph considering network contention and the two technologies. We evaluated the proposed algorithm by simulations and experiments with a multiprocessor system consisting of 4 PCs. In the experiment, the proposed algorithm produced a schedule that reduces the total execution time by 36% compared to conventional methods which are straightforward extensions of an existing method.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Lecture 4 principles of parallel algorithm design updatedVajira Thambawita
The main principles of parallel algorithm design are discussed here. For more information: visit, https://sites.google.com/view/vajira-thambawita/leaning-materials
The method of identifying similar groups of data in a data set is called clustering. Entities in each group are comparatively more similar to entities of that group than those of the other groups.
On selection of periodic kernels parameters in time series predictioncsandit
In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can
be used for the prediction task, performed as the typical regression problem. On the basis of the
Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic
kernels require the setting of their parameters it is necessary to analyse their influence on the
prediction quality. This paper describes an easy methodology of finding values of parameters of
periodic kernels. It is based on grid search. Two different error measures are taken into
consideration as the prediction qualities but lead to comparable results. The methodology was
tested on benchmark and real datasets and proved to give satisfactory results.
201907 AutoML and Neural Architecture SearchDaeJin Kim
Brief introduction of NAS
Review of EfficientNet (Google Brain), RandWire (FAIR) papers
NAS flow slide from KihoSuh's slideshare (https://www.slideshare.net/KihoSuh/neural-architecture-search-with-reinforcement-learning-76883153)
[References]
[1] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (https://arxiv.org/abs/1905.11946)
[2] Exploring Randomly Wired Neural Networks for Image Recognition (https://arxiv.org/abs/1904.01569)
발표자: 송환준(KAIST 박사과정)
발표일: 2018.8.
(Parallel Clustering Algorithm Optimization for Large-Scale Data Analytics)
Clustering은 데이터 분석에 가장 널리 쓰이는 방법 중 하나로 주어진 데이터를 유사성에 기초하여 여러 개의 그룹으로 나누는 작업이다. 하지만 Clustering 방법의 높은 계산 복잡도 때문에 대용량 데이터 분석에는 잘 사용되지 못하고 있다. 최근 이 높은 복잡도 문제를 해결하기 위해 많은 연구가 Hadoop, Spark와 같은 분산 컴퓨팅 방식을 적용하고 있지만 기존 Clustering 알고리즘을 분산 환경에 최적화시키는 것은 쉽지 않다. 특히, 효율성을 높이기 위해 정확성을 손실하는 문제 그리고 여러 작업자들 간의 부하 불균형 문제는 알고리즘을 분산처리 할 때 발생하는 대표적인 문제이다. 본 세미나에서는 대표적 Clustering 알고리즘인 DBSCAN을 분산처리 할 때 발생하는 여러 도전 과제에 초점을 맞추고 이를 해결 할 수 있는 새로운 해결책을 제시한다. 실제로 이 방법은 최신 연구의 방법과 비교하여 정확도 손실 없이 최대 180배까지 알고리즘의 성능을 향상시켰다.
본 세미나는 SIGMOD 2018에서 발표한 다음 논문에 대한 내용이다.
Song, H. and Lee, J., "RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning," In Proc. 2018 ACM Int'l Conf. on Management of Data (SIGMOD), Houston, Texas, pp. 1173 ~ 1187, June 2018
1. Background
- Concept of Clustering
- Concept of Distributed Processing (MapReduce)
- Clustering Algorithms (Focus on DBSCAN)
2. Challenges of Parallel Clustering
- Parallelization of Clustering Algorithm (Focus on DBSCAN)
- Existing Work
- Challenges
3. Our Approach
- Key Idea and Key Contribution
- Overview of Random Partitioning-DBSCAN
4. Experimental Results
5. Conclusions
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
3. Graph Structural Clustering
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3
• Graph Clustering
• group vertices into clusters: dense intra connection and sparse
inter connection
• Application
• do recommendations on social networks, web graphs and co-
purchasing graphs
• Graph Structural Clustering (Our Focus)
• utilize structural similarity among vertices for clustering
• identify clusters and vertex roles (cores, non-cores)
4. • Graph Structural Clustering (Our Focus)
• utilize structural similarity among vertices for clustering
• identify clusters and vertex roles (cores, non-cores: hubs, outliers)
Graph Structural Clustering Example
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4clusters
core
non-core
5. SCAN [Xu+, KDD’07]
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• Structural Similarity Computation
• based on neighbors of two vertices 𝑢 and 𝑣 (cosine measure):
• 𝑠𝑖𝑚 𝑢, 𝑣 = 𝑁 𝑢 ∩ 𝑁 𝑣 / |𝑁(𝑢)| ∙ |𝑁(𝑣)|
• 𝑢 and 𝑣 are similar neighbors, if
• they are connected
• their structural similarity 𝑠𝑖𝑚(𝑢, 𝑣) ≥ 𝜀
𝑁 6 = 5,6,7,8,9
𝑁 9 = 6,7,8,9,10,11
𝑠𝑖𝑚 6,9 = 4/ 5 ∙ 6 ≈ 0.73 similar:
𝑠𝑖𝑚(6,9) ≥ 0.6
𝜀 = 0.6, 𝜇 = 3
6. SCAN [Xu+, KDD’07]
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• Core Checking
• a vertex 𝑢 is a core, if it has ≥ 𝜇 similar neighbors
• Structural Clustering
• clustering by similar neighbors from cores
• adopting Breadth-First-Search (BFS) for cluster expansion
core
non-core
corenon-core
similar
not-similar
𝜀 = 0.6, 𝜇 = 3
7. • Structural Clustering
• clustering by similar neighbors from cores
• adopting Breadth-First-Search (BFS) for cluster expansion
SCAN [Xu+, KDD’07]
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7clusters
core
non-core
𝜀 = 0.6, 𝜇 = 3
9. Motivation of Parallelization
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On Twitter (0.7-billion-edge)
• Motivation of Parallelizing pSCAN
• cost too much time for interactive exploration of clustering results
• cost most from the set-intersection based similarity computation
11. Time BreakDown (SCAN and pSCAN)
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On LiveJournal/Orkut/Twitter
• Observations
• similarity computation is the performance bottleneck
• workload reduction of pSCAN is light-weight but useful
12. pSCAN Parallelization Challenge
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• Data Dependency
• there exists concurrent access of lower and upper bounds of
number of similar neighbors
• a priority queue for selecting vertex with max upper bound of
similar neighbors requires heavy synchronization
• owing to the similarity reuse technique, similarity values
𝒔𝒊𝒎(𝒖, 𝒗) and 𝒔𝒊𝒎(𝒗, 𝒖) are dependent
• Clustering Concurrency Issues
• union-find operations should be thread-safe
• cluster id initialization and assignment should be thread-safe
• Workload Skew and Irregularity
• the workload for each vertex depends on its degree and role
• pruning techniques make the workload irregular
14. Two-Step Multi-Phase Design Overview
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• Step 1: Role Computing (Determining Core or Non-Core)
• similarity pruning phase (without similarity computation)
• core checking phase
• core consolidating phase (finalizing roles of all the vertices)
• Step 2: Core and Non-Core Clustering
• core clustering without similarity computation phase
• finalizing core clustering with similarity computation phase
• cluster id initialization phase
• non-core clustering (cluster id assignment) phase
• Computation Optimizations
• degree-based task scheduling: dealing with the workload
skewness
• pivot-based set-intersection vectorization: improving the
efficiency of similarity computation
15. Step 1 : Role Computing
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core
non-core
finalizing vertex roles (either core or non-core),
stashing some similarity values (unknow/similar/not-similar)
16. Similarity Pruning
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• Utilize Similarity Definition & Min-Max Pruning
• do not incur set intersections (similarity computations)
• utilize lower and upper bounds of number of similar neighbors
similar
not-similar
𝑁 0 ∩ 𝑁 1 ≥ 2
𝑠𝑖𝑚(0,1) ≥ 2/ 4 ∙ 2 ≈ 0.71 > 𝜀
𝑁 9 ∩ 𝑁 11 ≤ 𝑁 11 = 2
𝑠𝑖𝑚(9,11) ≤ 2/ 6 ∙ 2 ≈ 0.58 < 𝜀
lower bound
upper bound
17. Similarity Pruning
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• Utilize Similarity Definition & Min-Max Pruning
• do not incur set intersections (similarity computations)
• utilize lower and upper bounds of number of similar neighbors
this phase determines
some vertex roles
without similarity
computations
lower bound
upper bound
local variables 𝒔𝒅 (similar degree) and 𝒆𝒅 (effective degree):
lower and upper bounds of 𝑢’s similar neighborhood size
18. • Vertex Exploration Order Constraint (𝒖 < 𝒗):
• to guarantee no redundant computation: each undirected edge is
computed at most once for the similarity value
• Core Checking and Consolidating Two-Phase
• to apply similarity reuse technique given vertex order constraint,
while finalizing all vertex roles
Core Checking and Consolidating
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after the two parallel phases,
all vertex roles are known
two phases
19. Core Checking
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two phases
1) initialize pruning related
lower and upper bounds, and
see if we can benefit from
parallel core checking from
𝑢’s neighbors with the min-
max pruning technique
21. Core Consolidating
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finalizing all vertex roles
two phases
work-efficiency-proof: the similarity computation is at most invoked once for
the similarity values 𝒔𝒊𝒎[𝒆(𝒖, 𝒗)] and 𝒔𝒊𝒎[𝒆(𝒗, 𝒖)]
22. Step 2 : Core and Non-Core Clustering
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core and non-core clustering
cluster 0
cluster 1
non-core
23. Core Clustering
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• Two Phase Separation
• core clustering using already known similarity values without set
intersections
• finalizing core clustering with set intersections
union-find pruning
vertex order constraint
vertex order constraint
• Avoiding Redundant Computation
• adding 𝑢 < 𝑣 constraint during the clustering for both phases
• applying union-find pruning in the second phase
two phases
24. Non-Core Clustering
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• Two Phase Separation
• cluster id initialization using atomic operations
• non-core cluster id assignment from all the cores to similar
neighbors (to form final results)
CAS atomic operations
cluster id assignment
25. Degree-based Task Scheduling
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degree-accumulation-
based-task-submission
handling tail task
• Dynamic Scheduling
• vertex computations relate to the degree and role of the vertex in all
the phases, for the exploration of neighbors and computations
• a task can be represented with 𝑣 𝑏𝑒𝑔, 𝑣 𝑒𝑛𝑑 , and the parameter of
range size is tunable (in our experimental setting: 32768)
Dynamic Scheduling Example for Core Checking
26. Set-Intersection Vectorization
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1. Find the first element in u’s
sorted neighbors ≥ pivot_v
2. Find the first element in v’s
sorted neighbors ≥ pivot_u
3. Count the match
upper and lower bounds
early termination
29. On KNL
Overall Performance (on CPU and KNL)
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On CPU
• Algorithm Comparison
• SCAN and pSCAN lack parallelization
• SCAN-XP lacks usage of pruning techniques
• anySCAN suffers from heavy synchronization and poor memory locality,
and runs out of memory on webbase and friendster datasets
• ppSCAN has good memory locality and negligible synchronization
overheads, and utilizes vectorization AVX2 on CPU and AVX512 on KNL
30. Set-Intersection Invocation Reduction
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• Work Efficiency
• multi-phase computation does not introduce more workload
• ppSCAN even compute less because of the similarity pruning and
parallel core checking benefits
𝝁 = 𝟏𝟎
𝝁 = 𝟓
31. Set-Intersection Vectorization Improvement
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𝝁 = 𝟓 (On Both CPU and KNL)
• Core Checking Speedup from Vectorization
• on CPU: at most 3.5x, on KNL: at most 4.5x
• vectorization have better performance with more workloads (when
memory access is not a bottleneck, e.g., when 𝜀 = 0.1 , intensive set
intersections hide the memory access latency
32. Scalability to Number of threads
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• Scalability and Time Breakdown
• all the four phases scale well to number of threads
• speedup of core checking is better than other phases, because the
intensive set intersection computations hide the memory access
latency
• time breakdown ratio:
• core checking and consolidating > similarity pruning > non-core
clustering > core clustering
𝜺 = 𝟎. 𝟐, 𝝁 = 𝟓 (On KNL)
33. Robustness
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• Given Different Parameters and Graphs
• robust on varying input parameters
• finish computations within 70 seconds
• robust on synthetic graphs (1-billion edge)
• finish computations within 60 seconds
• achieve up to 135x self-speedup on KNL
35. Conclusion
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• Parallelization & Vectorization Design
• multi-phase lock-free parallel vertex computations
• dynamic degree-based vertex computation task scheduling
• pivot-based set intersection vectorization
• Experimental Study
• ppSCAN is about 2x faster on KNL (64 cores, AVX512) than on
Xeon CPU (20 cores, AVX2) because of wider SIMD width
• on KNL, two orders of magnitude faster than the sequential pSCAN
• on KNL, one order of magnitude faster than the parallel anySCAN
and SCAN-XP
• on KNL, up to 135x self-speedup (over single-thread ppSCAN)
36. • Source Codes / Figures / Related Projects :
https://github.com/GraphProcessor/ppSCAN
• More Experimental Studies (Scripts / Figures):
https://github.com/GraphProcessor/ppSCAN/tree/master/pyt
hon_experiments
• This PPT:
https://www.dropbox.com/sh/i1r45o2ceraey8j/AAD8V3
WwPElQjwJ0-QtaKAzYa?dl=0&preview=ppSCAN.pdf
End - Q & A
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