Ron’s Code or Rivest Codes Scorecard Description RC2 RC4 RC5 RC6 Timeline 1987 1987 1994 1998 Type of Algorithm Block cipher Stream cipher Block cipher Block cipher Key size (in bits) 40 and 64 1 - 256 0 to 2040 bits (128 suggested) 128, 192, or 256 Variable key-size block cipher that was designed as a "drop-in" replacement for DES. Use Most widely used stream cipher based on a variable key-size Vernam stream cipher. It is often used in file encryption products and secure communications, such as within SSL. The cipher can be expected to run very quickly in software and is considered secure. © 2012 Cisco and/or its affiliates. All rights reserved. A fast block cipher that has a variable block size and key size. It can be used as a dropin replacement for DES if the block size is set to 64-bit. An AES finalist (Rijndael won). A 128-bit to 256- bit block cipher that was designed by Rivest, Sidney, and Yin and is based on RC5. Its main design goal was to meet the requirement of AES. 104
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ADDRESS MAPPING (MULTIPLE CHANNELS) C Row (14 bits) Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) C Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) Bank (3 bits) Column (11 bits) Byte in bus (3 bits) Row (14 bits) Bank (3 bits) C Column (11 bits) C Byte in bus (3 bits) • Where are consecutive cache blocks? C Row (14 bits) High Column Bank (3 bits) C High Column Bank (3 bits) High Column C Bank (3 bits) High Column Bank (3 bits) High Column 8 bits Low Col. Byte in bus (3 bits) C Low Col. Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Low Col. 3 bits 8 bits Row (14 bits) Byte in bus (3 bits) 3 bits 8 bits Row (14 bits) Low Col. Bank (3 bits) Low Col. C Byte in bus (3 bits) 3 bits 47
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Stream Cipher Properties  some design considerations are:    long period with no repetitions statistically random depends on large enough key (current recommendation: >= 128 bits)  properly designed, can be as secure as a block cipher with same size key  but usually simpler & faster
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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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• Common security tools • AES-CTR mode 1 – Confidentiality & integrity scheme 2 – Hardware security management 3 – Security cost AES-GCM: Asolution counter based 4 – End to end checking • Fast Integrity Checking with AESGCM • Confidentiality & integrity in action mode with a low with latency integrity • Comparison previous work IV || @ 32 || IV96 || 96 || @32 TS TS32 32 128 bit 128 bit TS+ TS+ 1 1 128 bit IV || @ 32 || IV96 || 96 || @32 (TS+1) (TS+1)32 32 128 bit 128-bit 128-bit AES AES 128 bit 128-bit 128-bit AES AES 128 bit Plaintext Plaintext 1 1 128 bit 128 bit Plaintext Plaintext 2 2 128 bit ENCRYPTION & DECRYPTION CIRCUITRY 128 bit Ciphertext Ciphertext 1 1 Ciphertext Ciphertext 2 2 128 bit 128 bit Mult MultHH 128 bit 0064 || Len(C) 64 64 || Len(C)64 Mult MultHH 128 bit 128 bit 128 bit AUTHENTICATION CIRCUITRY Mult MultHH 128 bit Tag Tag 12
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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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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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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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1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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Cipher Block Chaining • cipher block: if input block repeated, will produce same cipher text: t=1 … m(17) = “HTTP/1.1” t=17 • cipher block chaining: XOR ith input block, m(i), with previous block of cipher text, c(i-1) – c(0) transmitted to receiver in clear m(1) = “HTTP/1.1” block cipher c(1) block cipher c(17) m(i) c(i-1) + block cipher c(i) = “k329aM02” = “k329aM02”
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