Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks! Dynamically Rearranging Synteny Blocks in Comparative Genomes Nick Egan’s Final Project Presentation for BIO 131 Intro to Computational Biology Taught by Anna Ritz
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Modes of Operation  block ciphers encrypt fixed size blocks  eg. DES encrypts 64-bit blocks with 56-bit key  need some way to en/decrypt arbitrary amounts of data in practice  ANSI X3.106-1983 Modes of Use (now FIPS 81) defines 4 possible modes  There are 5 modes that are in common use
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Preamble “Post-amble” Block Execution: 3 Detail Observing Block Observing Block “Post-amble” “Post-amble” 3 Observing Block Observing Block ok Measurement Set ready “Post-amble” EVLA Data Processing PDR Observing Observing Block Block Observing Observing Block Block Failed! Preamble “Post-amble” Preamble ok ?4 5 Preamble ready Preamble Observing Observing Block Block Observing Observing Block Block Observing Block Observing Block Measurement Set “Post-amble” “Post-amble” Preamble Preamble “Post-amble” Measurement Set “Post-amble” “Post-amble” “Post-amble” July 18 - 19, 2002 2 2 Observing Observing Block Block Block Observing Observing Observing Block Block ok Archive: Preamble Observing Block Observing Block 34 ready Preamble “Post-amble” 1 3 Observing Block Observing Observing Block Block Observing Block Observing Observing Block Block ready Preamble Execution: Preamble ready Observing Observing Block Block Observing Observing Block Block Preamble Observing Block Observing Block 22 “Post-amble” “Post-amble” Preamble Preamble 1 “Post-amble” Preamble Input Queue: ok Measurement Set Boyd Waters 13
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Crossbar 7 1-bit 16-bit 16-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 16 1-bit 16-bit iterations 16-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 16-bit 1-bit 16-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 16-bit 1-bit 16-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit 2-bit Input Neurons
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Communication In a parallel implementation of simple search, tasks can execute independently and need communicate only to report solutions. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 174 174 CS 340 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 200 200 The parallel algorithm for this problem will also need to keep track of the bounding value (i.e., the smallest chip area found so far), which must be accessed by every task. One possibility would be to encapsulate the bounding value maintenance in a single centralized task with which the other tasks will communicate. This approach is inherently unscalable, since the processor handling the centralized task can only service requests from the other tasks at a particular rate, thus bounding the number of tasks that can execute concurrently. Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 Page 6
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Partitioning There is no obvious data structure that could be used to perform a decomposition of this problem’s domain into components that could be mapped to separate processors. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 200 200 Chip Chip Size: Size: 174 174 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 A fine-grained functional decomposition is therefore needed, where the exploration of each search tree node is handled by a separate task. CS 340 This means that new tasks will be created in a wavefront as the search progresses down the search tree, which will be explored in a breadthfirst fashion. Notice that only tasks on the wavefront will be able to execute concurrently. Page 5
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Transparent Scalability  Hardware is free to assign blocks to any SM (processor)  A kernel scales across any number of parallel processors Device Kernel grid Device Block 0 Block 1 Block 2 Block 3 Block 0 Block 1 Block 4 Block 5 Block 6 Block 7 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 26 time Block 0 Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 Each block can execute in any order relative to other blocks.
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Transparent Scalability Hardware is free to assign blocks to any processor at any time  A kernel scales across any number of parallel processors Device Device Kernel grid Block 0 Block 1 Block 2 Block 3 Block 0 Block 2 Block 1 Block 3 Block 4 Block 5 Block 6 Block 7  Block 4 Block 5 Block 6 Block 7 time Block 0 Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 Each block can execute in any CUDA Tools and Threads – Slide order relative 69
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HDFS (Hadoop Distributed File System) is a distr file sys for commodity hdwr. Differences from other distr file sys are few but significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides hi thruput access to app data and is suitable for apps that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS originally was infrastructure for Apache Nutch web search engine project, is part of Apache Hadoop Core http://hadoop.apache.org/core/ 2.1. Hardware Failure Hardware failure is the normal. An HDFS may consist of hundreds or thousands of server machines, each storing part of the file system’s data. There are many components and each component has a non-trivial prob of failure means that some component of HDFS is always non-functional. Detection of faults and quick, automatic recovery from them is core arch goal of HDFS. 2.2. Streaming Data Access Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements not needed for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates. 2.3. Large Data Sets Apps on HDFS have large data sets, typically gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It provides high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It supports ~10 million files in a single instance. 2.4. Simple Coherency Model: HDFS apps need a write-once-read-many access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A Map/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in future [write once read many at file level] 2.5. “Moving Computation is Cheaper than Moving Data” A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where the app is running. HDFS provides interfaces for applications to move themselves closer to where the data is located. 2.6. Portability Across Heterogeneous Hardware and Software Platforms: HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. 3. NameNode and DataNodes: HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is 1 blocks stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction The NameNode and DataNode are pieces of software designed to run on commodity machines, typically run GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode. 4. The File System Namespace: HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features. The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This info is stored by NameNode. 5. Data Replication: HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode
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Fully Associative Cache 5-bitoffset offsetsupports supports 5-bit 32bytes bytesper perblock block 32 Fully-associative cache does not need Set Index field. Index=0 corresponding to one set. Address Addressisispartitioned partitionedinto into • Block address • Block address • Block offset which identifies the data • Block offset which identifies the data within withinthe theblock block • Block can go anywhere in the cache • Block can go anywhere in the cache • Must examine all blocks • Must examine all blocks • Each cache block has a Tag • Each cache block has a Tag • Tag is compared to block number • Tag is compared to block number • If one of the blocks has Tag=Block # • If one of the blocks has Tag=Block # we wehave haveaahit hit • Need a comparator per cache block • Need a comparator per cache block • Comparisons performed in parallel • Comparisons performed in parallel • • Spring 2016, arz CPE555A – Real-Time Embedded Systems Stevens Institute of Technology 42
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NeMoFinder adapts SPIN [27] to extract frequent trees and expands them into non-isomorphic graphs.[8] NeMoFinder utilizes frequent size-n trees to partition the input network into a collection of size-n graphs, afterward finding frequent size-n sub-graphs by expansion of frequent trees edge-by-edge until getting a complete size-n graph Kn. The algorithm finds NMs in undirected networks and is not limited to extracting only induced sub-graphs. Furthermore, NeMoFinder is an exact enumeration algorithm and is not based on a sampling method. As Chen et al. claim, NeMoFinder is applicable for detecting relatively large NMs, for instance, finding NMs up to size-12 from the whole S. cerevisiae (yeast) PPI network as the authors claimed.[28] NeMoFinder consists of three main steps. First, finding frequent size-n trees, then utilizing repeated size-n trees to divide the entire network into a collection of size-n graphs, finally, performing sub-graph join operations to find frequent size-n sub-graphs.[26] In the first step, the algorithm detects all non-isomorphic size-n trees and mappings from a tree to the network. In the second step, the ranges of these mappings are employed to partition the network into size-n graphs. Up to this step, there is no distinction between NeMoFinder and an exact enumeration method. However, a large portion of non-isomorphic size-n graphs still remain. NeMoFinder exploits a heuristic to enumerate non-tree size-n graphs by the obtained information from preceding steps. The main advantage is in third step, which generates candidate sub-graphs from previously enumerated sub-graphs. This generation of new size-n sub-graphs is done by joining each previous sub-graph with derivative sub-graphs from itself called cousin sub-graphs. These new sub-graphs contain one additional edge in comparison to the previous sub-graphs. However, there exist some problems in generating new sub-graphs: There is no clear method to derive cousins from a graph, joining a sub-graph with its cousins leads to redundancy in generating particular sub-graph more than once, and cousin determination is done by a canonical representation of the adjacency matrix which is not closed under join operation. NeMoFinder is an efficient network motif finding algorithm for motifs up to size 12 only for protein-protein interaction networks, which are presented as undirected graphs. And it is not able to work on directed networks which are so important in the field of complex and biological networks. The pseudocode of NeMoFinder is shown here: NeMoFinder Input: G - PPI network; N - Number of randomized networks; K - Maximal network motif size; F - Frequency threshold; S - Uniqueness threshold; Output: U - Repeated and unique network motif set; D ← ∅; for motif-size k from 3 to K do T ← FindRepeatedTrees(k); GDk ← GraphPartition(G, T) D ← D ∪ T; D′ ← T; i ← k; while D″ = ∅ and i ≤ k × (k - 1) / 2 do D′ ← FindRepeatedGraphs(k, i, D′); D ← D ∪ D′; i ← i + 1; end while end for for counter i from 1 to N do Grand ← RandomizedNetworkGeneration(); for each g ∈ D do GetRandFrequency(g, Grand); end for end for U ← ∅; for each g ∈ D do s ← GetUniqunessValue(g); if s ≥ S then U ← U ∪ {g}; end if end for return U Grochow and Kellis [29] proposed an exact alg for enumerating sub-graph appearances, which is based on a motif-centric approach, which means that the frequency of a given sub-graph,called the query graph, is exhaustively determined by searching for all possible mappings from the query graph into the larger network. It is claimed [29] that a motif-centric method in comparison to network-centric methods has some beneficial features. First of all it avoids the increased complexity of sub-graph enumeration. Also, by using mapping instead of enumerating, it enables an improvement in the isomorphism test. To improve the performance of the alg, since it is an inefficient exact enumeration alg, the authors introduced a fast method which is called symmetry-breaking conditions. During straightforward sub-graph isomorphism tests, a sub-graph may be mapped to the same sub-graph of the query graph multiple times. In Grochow-Kellis alg symmetrybreaking is used to avoid such multiple mappings. GK alg and symmetry-breaking condition which eliminates redundant isomorphism tests. (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set of symmetrybreaking conditions of G given by SymG in (c). Only the first mapping in AutG satisfies the SynG conditions; so, by applying SymG in Isomorphism Extension module alg only enumerate each match-able sub-graph to G once. Note that SynG is not a unique set for an arbitrary graph G. The GK alg discovers the whole set of mappings of a given query graph to the network in two major steps. It starts with the computation of symmetry-breaking conditions of the query graph. Next, by means of a branch-and-bound method, alg tries to find every possible mapping from the query graph to the network that meets the associated symmetry-breaking conditions. Computing symmetry-breaking conditions requires finding all automorphisms of a given query graph. Even though, there is no efficient (or polynomial time) algorithm for the graph automorphism problem, this problem can be tackled efficiently in practice by McKay’s tools.[24][25] As it is claimed, using symmetry-breaking conditions in NM detection lead to save a great deal of running time. Moreover, it can be inferred from the results in [29][30] that using (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set the symmetry-breaking conditions results in high efficiency particularly for directed networks in comparison to undirected of symmetry-breaking conditions of G given by SymG networks. The symmetry-breaking conditions used in the GK algorithm are similar to the restriction which ESU algorithm in (c). Only the first mapping in AutG satisfies the applies to the labels in EXT and SUB sets. In conclusion, the GK algorithm computes the exact number of appearance of a SynG conditions; as a result, by applying SymG in the given query graph in a large complex network and exploiting symmetry-breaking conditions improves the algorithm Isomorphism Extension module the algorithm only performance. Also, GK alg is 1 of the known algorithms having no limitation for motif size in implementation and potentially enumerate each match-able sub-graph in network to G once. SynG is not a unique set for an arbitrary graph G. it can find motifs of any size.
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Encryption Algorithms • Types of Encryption Algorithms – Block cipher – encrypts groups of text at a time • A block cipher encrypts the whole word cat instead of each letter – Stream cipher – encrypts cleartext one bit at a time • The letters c, a, and t in cat are encrypted separately – Symmetric algorithms – use the same key to encrypt and decrypt a message • Faster, more efficient method – Asymmetric algorithms – use a specially generated key pair • One key encrypts cleartext into ciphertext and other key decrypts Guide to Network Defense and Countermeasures, 3rd Edition © Cengage Learning 2014 12
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Confidentiality, Authentication, Non-Repudiation Public Key Encryption Examples: RSA, ECC, Quantum Sender, Receiver have Complimentary Keys Plaintext = Decrypt(kPRIV, Encrypt(kPUB,Plaintext)) Joe Joe Encrypt Kpublic Decrypt Kpublic Encryption (e.g., RCS) Message, private key Authentication, Non-repudiation Digital Signature Decrypt Kprivate Encrypt Kprivate Key owner Key owner Plaintext = Decrypt(kPUB, Encrypt(kPRIV,Plaintext)) NIST Recommended: 2011: RSA 2048 bit
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2000 TinyHMS 1000 0 105 1.5 x 10 105.2 105.4 105.6 105.8 106 106.2 106.4 106.6 4 0.5 105 105.2 107 accX accY accZ Motion Sensor (TS2) 1 106.8 105.4 105.6 105.8 106 106.2 106.4 106.6 106.8 107 105.4 105.6 105.8 106 106.2 106.4 106.6 106.8 107 4000 ECG Sensor (TS1) 3000 2000 1000 105 105.2 Heart Beat Beacon Message Heart Beat Event Message with Timestamp Heart Beat Step Step Beacon Message … NC TS1 TS2 TS3 … NC Frame i-1 TS1 TS2 TS3 Frame i 43
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