Highlighted notes while studying Concurrent Data Structures:
DDR3 SDRAM
Source: Wikipedia
Double Data Rate 3 Synchronous Dynamic Random-Access Memory, officially abbreviated as DDR3 SDRAM, is a type of synchronous dynamic random-access memory (SDRAM) with a high bandwidth ("double data rate") interface, and has been in use since 2007. It is the higher-speed successor to DDR and DDR2 and predecessor to DDR4 synchronous dynamic random-access memory (SDRAM) chips. DDR3 SDRAM is neither forward nor backward compatible with any earlier type of random-access memory (RAM) because of different signaling voltages, timings, and other factors.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
DDR4 SDRAM
Source: Wikipedia
Double Data Rate 4 Synchronous Dynamic Random-Access Memory, officially abbreviated as DDR4 SDRAM, is a type of synchronous dynamic random-access memory with a high bandwidth ("double data rate") interface.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
DDR SDRAM
Source: Wikipedia
Double Data Rate Synchronous Dynamic Random-Access Memory, officially abbreviated as DDR SDRAM, is a double data rate (DDR) synchronous dynamic random-access memory (SDRAM) class of memory integrated circuits used in computers. DDR SDRAM, also retroactively called DDR1 SDRAM, has been superseded by DDR2 SDRAM, DDR3 SDRAM, and DDR4 SDRAM, and soon will be superseded by DDR5 SDRAM. None of its successors are forward or backward compatible with DDR1 SDRAM, meaning DDR2, DDR3, DDR4 and DDR5 memory modules will not work in DDR1-equipped motherboards, and vice versa.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
DDR4 SDRAM
Source: Wikipedia
Double Data Rate 4 Synchronous Dynamic Random-Access Memory, officially abbreviated as DDR4 SDRAM, is a type of synchronous dynamic random-access memory with a high bandwidth ("double data rate") interface.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
DDR SDRAM
Source: Wikipedia
Double Data Rate Synchronous Dynamic Random-Access Memory, officially abbreviated as DDR SDRAM, is a double data rate (DDR) synchronous dynamic random-access memory (SDRAM) class of memory integrated circuits used in computers. DDR SDRAM, also retroactively called DDR1 SDRAM, has been superseded by DDR2 SDRAM, DDR3 SDRAM, and DDR4 SDRAM, and soon will be superseded by DDR5 SDRAM. None of its successors are forward or backward compatible with DDR1 SDRAM, meaning DDR2, DDR3, DDR4 and DDR5 memory modules will not work in DDR1-equipped motherboards, and vice versa.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
In the project#1, IBM 130nm process is used to design and manual layout a 128 word SRAM, with word size 10bits. Cadence's Virtuoso is applied for layout editing, DRC and LVS running and circuit simulation.
What is RAM? Why is it called Random Access Memory? What does Volatile & Non-Volatile Memory Means? What is SAM? Why is it called Sequential Access Memory? Other uses of RAM? What is Virtual Memory? What is RAM Disk? What is Shadow RAM?... By the end of this Presentation you will be able to answer these questions, so start Sliding the Slides!
HBM stands for high bandwidth memory and is a type of memory interface used in 3D-stacked DRAM (dynamic random access memory) in GPUs, as well as the server, machine-learning DSP , high-performance computing and networking and client space.
Here is a slide on Random Access Memory, slide consists of detailed presentation on primary Memory,types and history of RAM. Hope you will Enjoy the slide.
Highlighted notes while studying Concurrent Data Structures:
High Bandwidth Memory
Source: Wikipedia
High Bandwidth Memory (HBM) is a high-speed computer memory interface for 3D-stacked SDRAM from Samsung, AMD and SK Hynix. It is used in conjunction with high-performance graphics accelerators, network devices and in some supercomputers. (Such as the NEC SX-Aurora TSUBASA and Fujitsu A64FX) [1] The first HBM memory chip was produced by SK Hynix in 2013, [2] and the first devices to use HBM were the AMD Fiji GPUs in 2015. [3][4]
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
GDDR4 SDRAM
Source: Wikipedia
GDDR4 SDRAM, an abbreviation for Graphics Double Data Rate 4 Synchronous Dynamic Random-Access Memory, is a type of graphics card memory specified by the JEDEC Semiconductor Memory Standard. [1][2] It is a rival medium to Rambus's XDR DRAM. GDDR4 is based on DDR3 SDRAM technology and was intended to replace the DDR2-based GDDR3, but it ended up being replaced by GDDR5 within a year.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
In the project#1, IBM 130nm process is used to design and manual layout a 128 word SRAM, with word size 10bits. Cadence's Virtuoso is applied for layout editing, DRC and LVS running and circuit simulation.
What is RAM? Why is it called Random Access Memory? What does Volatile & Non-Volatile Memory Means? What is SAM? Why is it called Sequential Access Memory? Other uses of RAM? What is Virtual Memory? What is RAM Disk? What is Shadow RAM?... By the end of this Presentation you will be able to answer these questions, so start Sliding the Slides!
HBM stands for high bandwidth memory and is a type of memory interface used in 3D-stacked DRAM (dynamic random access memory) in GPUs, as well as the server, machine-learning DSP , high-performance computing and networking and client space.
Here is a slide on Random Access Memory, slide consists of detailed presentation on primary Memory,types and history of RAM. Hope you will Enjoy the slide.
Highlighted notes while studying Concurrent Data Structures:
High Bandwidth Memory
Source: Wikipedia
High Bandwidth Memory (HBM) is a high-speed computer memory interface for 3D-stacked SDRAM from Samsung, AMD and SK Hynix. It is used in conjunction with high-performance graphics accelerators, network devices and in some supercomputers. (Such as the NEC SX-Aurora TSUBASA and Fujitsu A64FX) [1] The first HBM memory chip was produced by SK Hynix in 2013, [2] and the first devices to use HBM were the AMD Fiji GPUs in 2015. [3][4]
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Highlighted notes while studying Concurrent Data Structures:
GDDR4 SDRAM
Source: Wikipedia
GDDR4 SDRAM, an abbreviation for Graphics Double Data Rate 4 Synchronous Dynamic Random-Access Memory, is a type of graphics card memory specified by the JEDEC Semiconductor Memory Standard. [1][2] It is a rival medium to Rambus's XDR DRAM. GDDR4 is based on DDR3 SDRAM technology and was intended to replace the DDR2-based GDDR3, but it ended up being replaced by GDDR5 within a year.
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Design, Validation and Correlation of Characterized SODIMM Modules Supporting...IOSR Journals
Abstract : In any computing environment, it is necessary for the processor to have fast accessible RAM that allows temporary storage of data. DDR3- SODIMM module is a key component in the memory interface and is becoming increasingly important in enabling higher speeds. Considering higher bandwidths and speeds more than 1GHz, DDR3 is enabling poses more and more high speed signaling and design challenges. Characterized SODIMM module need to be designed to understand and analyze the impact of SODIMM parameters at higher speeds and thereby define more robust memory interface. This will include simulation, board design, validation and results correlation and involves high speed simulation and validation methodologies. Keywords – Validation, Correlation, DDR3, Characterized SODIMM, Signal Integrity
Solid State Drives - Seminar for Computer Engineering Semester 6 - VIT,Univer...ravipbhat
Solid state is term that refers to electronic circuitry that is built entirely out of semiconductors.
A Solid-State Drive (SSD) is a data storage device that uses solid state memory to store persistent data and SSDs use same I/O interfaces developed for hard disk drives.
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...Subhajit Sahu
TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
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.
Adjusting Bitset for graph : SHORT REPORT / NOTESSubhajit Sahu
Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Experiments with Primitive operations : SHORT REPORT / NOTESSubhajit Sahu
This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...Subhajit Sahu
Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTESSubhajit Sahu
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].
A Dynamic Algorithm for Local Community Detection in Graphs : NOTESSubhajit Sahu
**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTESSubhajit Sahu
A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.
Application Areas of Community Detection: A Review : NOTESSubhajit Sahu
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Survey for extra-child-process package : NOTESSubhajit Sahu
Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTERSubhajit Sahu
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/692d263f463fd49be6eb5aa65dd4d0f9
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...Subhajit Sahu
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Fast Incremental Community Detection on Dynamic Graphs : NOTESSubhajit Sahu
In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a
Google Calendar is a versatile tool that allows users to manage their schedules and events effectively. With Google Calendar, you can create and organize calendars, set reminders for important events, and share your calendars with others. It also provides features like creating events, inviting attendees, and accessing your calendar from mobile devices. Additionally, Google Calendar allows you to embed calendars in websites or platforms like SlideShare, making it easier for others to view and interact with your schedules.
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
1. 02/10/2020 DDR3 SDRAM - Wikipedia
https://en.wikipedia.org/wiki/DDR3_SDRAM 1/9
DDR3 SDRAM
4 GiB PC3-12800 ECC DDR3 DIMM
Type Synchronous dynamic random-
access memory (SDRAM)
Release
date
2007
Predecessor DDR2 SDRAM (2003)
Successor DDR4 SDRAM (2014)
DDR3 SDRAM
Double Data Rate 3 Synchronous Dynamic Random-Access
Memory, officially abbreviated as DDR3 SDRAM, is a type of
synchronous dynamic random-access memory (SDRAM) with a
high bandwidth ("double data rate") interface, and has been in use
since 2007. It is the higher-speed successor to DDR and DDR2
and predecessor to DDR4 synchronous dynamic random-access
memory (SDRAM) chips. DDR3 SDRAM is neither forward nor
backward compatible with any earlier type of random-access
memory (RAM) because of different signaling voltages, timings,
and other factors.
DDR3 is a DRAM interface specification. The actual DRAM
arrays that store the data are similar to earlier types, with similar
performance. The primary benefit of DDR3 SDRAM over its
immediate predecessor, DDR2 SDRAM, is its ability to transfer
data at twice the rate (eight times the speed of its internal memory
arrays), enabling higher bandwidth or peak data rates.
The DDR3 standard permits DRAM chip capacities of up to 8 gibibits (Gibit), and up to four ranks of 64 bits each for
a total maximum of 16 gibibytes (GiB) per DDR3 DIMM. Because of a hardware limitation not fixed until Ivy
Bridge-E in 2013, most older Intel CPUs only support up to 4-Gibit chips for 8 GiB DIMMs (Intel's Core 2 DDR3
chipsets only support up to 2 Gibit). All AMD CPUs correctly support the full specification for 16 GiB DDR3
DIMMs.[1]
History
Successor
Specification
Overview
Dual-inline memory modules
Latencies
Power consumption
Modules
Serial presence detect
Release 4
XMP extension
Variants
DDR3L and DDR3U extensions
Feature summary
Components
Modules
Technological advantages over DDR2
See also
Notes
References
External links
Contents
2. 02/10/2020 DDR3 SDRAM - Wikipedia
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In February 2005, Samsung introduced the first prototype DDR3 memory chip. Samsung played a major role in the
development and standardisation of DDR3.[2][3] In May 2005, Desi Rhoden, chairman of the JEDEC committee,
stated that DDR3 had been under development for "about 3 years".[4]
DDR3 was officially launched in 2007, but sales were not expected to overtake DDR2 until the end of 2009, or
possibly early 2010, according to Intel strategist Carlos Weissenberg, speaking during the early part of their roll-out
in August 2008.[5] (The same timescale for market penetration had been stated by market intelligence company
DRAMeXchange over a year earlier in April 2007,[6] and by Desi Rhoden in 2005.[4]) The primary driving force
behind the increased usage of DDR3 has been new Core i7 processors from Intel and Phenom II processors from
AMD, both of which have internal memory controllers: the former requires DDR3, the latter recommends it. IDC
stated in January 2009 that DDR3 sales would account for 29% of the total DRAM units sold in 2009, rising to 72%
by 2011.[7]
In September 2012, JEDEC released the final specification of DDR4.[8] The primary benefits of DDR4 compared to
DDR3 include a higher standardized range of clock frequencies and data transfer rates[9] and significantly lower
voltage.
Compared to DDR2 memory, DDR3 memory uses less power. Some manufacturers further propose using "dual-gate"
transistors to reduce leakage of current.[10]
According to JEDEC,[11]:111 1.575 volts should be considered the absolute maximum when memory stability is the
foremost consideration, such as in servers or other mission-critical devices. In addition, JEDEC states that memory
modules must withstand up to 1.80 volts[a] before incurring permanent damage, although they are not required to
function correctly at that level.[11]:109
Another benefit is its prefetch buffer, which is 8-burst-deep. In contrast, the prefetch buffer of DDR2 is 4-burst-deep,
and the prefetch buffer of DDR is 2-burst-deep. This advantage is an enabling technology in DDR3's transfer speed.
DDR3 modules can transfer data at a rate of 800–2133 MT/s using both rising and falling edges of a 400–1066 MHz
I/O clock. This is twice DDR2's data transfer rates (400–1066 MT/s using a 200–533 MHz I/O clock) and four times
the rate of DDR (200–400 MT/s using a 100–200 MHz I/O clock). High-performance graphics was an initial driver of
such bandwidth requirements, where high bandwidth data transfer between framebuffers is required.
Because the hertz is a measure of cycles per second, and no signal cycles more often than every other transfer,
describing the transfer rate in units of MHz is technically incorrect, although very common. It is also misleading
because various memory timings are given in units of clock cycles, which are half the speed of data transfers.
DDR3 does use the same electric signaling standard as DDR and DDR2, Stub Series Terminated Logic, albeit at
different timings and voltages. Specifically, DDR3 uses SSTL_15.[13]
In February 2005, Samsung demonstrated the first DDR3 memory prototype, with a capacity of 512 Mb and a
bandwidth of 1.066 Gbps.[2] Products in the form of motherboards appeared on the market in June 2007[14] based on
Intel's P35 "Bearlake" chipset with DIMMs at bandwidths up to DDR3-1600 (PC3-12800).[15] The Intel Core i7,
released in November 2008, connects directly to memory rather than via a chipset. The Core i7, i5 & i3 CPUs
History
Successor
Specification
Overview
3. 02/10/2020 DDR3 SDRAM - Wikipedia
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Physical comparison of DDR, DDR2, and DDR3 SDRAM
Desktop PCs (DIMM)
Notebook and convertible PCs
(SO-DIMM)
initially supported only DDR3. AMD's socket AM3
Phenom II X4 processors, released in February 2009,
were their first to support DDR3 (while still supporting
DDR2 for backwards compatibility).
DDR3 dual-inline memory modules (DIMMs) have 240
pins and are electrically incompatible with DDR2. A key
notch—located differently in DDR2 and DDR3 DIMMs
—prevents accidentally interchanging them. Not only are
they keyed differently, but DDR2 has rounded notches on
the side and the DDR3 modules have square notches on
the side.[16] DDR3 SO-DIMMs have 204 pins.[17]
For the Skylake microarchitecture, Intel has also designed
a SO-DIMM package named UniDIMM, which can use
either DDR3 or DDR4 chips. The CPU's integrated
memory controller can then work with either. The purpose
of UniDIMMs is to handle the transition from DDR3 to
DDR4, where pricing and availability may make it
desirable to switch RAM type. UniDIMMs have the same
dimensions and number of pins as regular DDR4 SO-
DIMMs, but the notch is placed differently to avoid
accidentally using in an incompatible DDR4 SO-DIMM
socket.[18]
DDR3 latencies are numerically higher because the I/O
bus clock cycles by which they are measured are shorter;
the actual time interval is similar to DDR2 latencies,
around 10 ns. There is some improvement because DDR3
generally uses more recent manufacturing processes, but
this is not directly caused by the change to DDR3.
CAS latency (ns) = 1000 × CL (cycles) ÷ clock frequency (MHz) = 2000 × CL (cycles) ÷ transfer rate (MT/s)
While the typical latencies for a JEDEC DDR2-800 device were 5-5-5-15 (12.5 ns), some standard latencies for
JEDEC DDR3 devices include 7-7-7-20 for DDR3-1066 (13.125 ns) and 8-8-8-24 for DDR3-1333 (12 ns).
As with earlier memory generations, faster DDR3 memory became available after the release of the initial versions.
DDR3-2000 memory with 9-9-9-28 latency (9 ns) was available in time to coincide with the Intel Core i7 release in
late 2008,[19] while later developments made DDR3-2400 widely available (with CL 9–12 cycles = 7.5–10 ns), and
speeds up to DDR3-3200 available (with CL 13 cycles = 8.125 ns).
Power consumption of individual SDRAM chips (or, by extension, DIMMs) varies based on many factors, including
speed, type of usage, voltage, etc. Dell's Power Advisor calculates that 4 GB ECC DDR1333 RDIMMs use about
4 W each.[20] By contrast, a more modern mainstream desktop-oriented part 8 GB, DDR3/1600 DIMM, is rated at
2.58 W, despite being significantly faster.[21]
Dual-inline memory modules
Latencies
Power consumption
Modules
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Name Chip Bus Timings
Standard Type Module
Clock
rate
(MHz)
Cycle
time
(ns)[22]
Clock
rate
(MHz)
Transfer
rate
(MT/s)
Bandwidth
(MB/s)
CL-
TRCD-
TRP
CAS
latency
(ns)
DDR3-800
D PC3-
6400
100 10 400 800 6400
5-5-5 12.5
E 6-6-6 15
DDR3-1066
E
PC3-
8500
133⅓ 7.5 533⅓ 1066.67 8533⅓
6-6-6 11.25
F 7-7-7 13.125
G 8-8-8 15
DDR3-1333
F*
PC3-
10600
166⅔ 6 666⅔ 1333⅓ 10666.67
7-7-7 10.5
G 8-8-8 12
H 9-9-9 13.5
J*
10-10-
10
15
DDR3-1600
G*
PC3-
12800
200 5 800 1600 12800
8-8-8 10
H 9-9-9 11.25
J
10-10-
10
12.5
K
11-11-
11
13.75
DDR3-1866
DDR3-
1866J*
DDR3-
1866K
DDR3-
1866L
DDR3-
1866M*
PC3-
14900
233⅓ 4.286 933⅓ 1866⅔ 14933⅓
10-10-
10
11-11-
11
12-12-
12
13-13-
13
10.56
11.786
12.857
13.929
DDR3-2133
DDR3-
2133K*
DDR3-
2133L
DDR3-
2133M
DDR3-
2133N*
PC3-
17000
266⅔ 3.75 1066⅔ 2133⅓ 17066⅔
11-11-
11
12-12-
12
13-13-
13
14-14-
14
10.313
11.25
12.188
13.125
* optional
DDR3-xxx denotes data transfer rate, and describes DDR chips, whereas PC3-xxxx denotes theoretical bandwidth
(with the last two digits truncated), and is used to describe assembled DIMMs. Bandwidth is calculated by taking
transfers per second and multiplying by eight. This is because DDR3 memory modules transfer data on a bus that is
64 data bits wide, and since a byte comprises 8 bits, this equates to 8 bytes of data per transfer.
With two transfers per cycle of a quadrupled clock signal, a 64-bit wide DDR3 module may achieve a transfer rate of
up to 64 times the memory clock speed. With data being transferred 64 bits at a time per memory module, DDR3
SDRAM gives a transfer rate of (memory clock rate) × 4 (for bus clock multiplier) × 2 (for data rate) × 64 (number of
bits transferred) / 8 (number of bits in a byte). Thus with a memory clock frequency of 100 MHz, DDR3 SDRAM
gives a maximum transfer rate of 6400 MB/s.
The data rate (in MT/s) is twice the I/O bus clock (in MHz) due to the double data rate of DDR memory. As
explained above, the bandwidth in MB/s is the data rate multiplied by eight.
CL – CAS Latency clock cycles, between sending a column address to the memory and the beginning of the data in
response
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tRCD – Clock cycles between row activate and reads/writes
tRP – Clock cycles between row precharge and activate
Fractional frequencies are normally rounded down, but rounding up to 667 is common because of the exact number
being 666⅔ and rounding to the nearest whole number. Some manufacturers also round to a certain precision or
round up instead. For example, PC3-10666 memory could be listed as PC3-10600 or PC3-10700.[23]
Note: All items listed above are specified by JEDEC as JESD79-3F.[11]:157–165All RAM data rates in-between or
above these listed specifications are not standardized by JEDEC—often they are simply manufacturer optimizations
using higher-tolerance or overvolted chips. Of these non-standard specifications, the highest reported speed reached
was equivalent to DDR3-2544, as of May 2010.[24]
Alternative naming: DDR3 modules are often incorrectly labeled with the prefix PC (instead of PC3), for marketing
reasons, followed by the data-rate. Under this convention PC3-10600 is listed as PC1333.[25]
DDR3 memory utilizes serial presence detect.[26] Serial presence detect (SPD) is a standardized way to automatically
access information about a computer memory module, using a serial interface. It is typically used during the power-
on self-test for automatic configuration of memory modules.
Release 4 of the DDR3 Serial Presence Detect (SPD) document (SPD4_01_02_11) adds support for Load Reduction
DIMMs and also for 16b-SO-DIMMs and 32b-SO-DIMMs.
JEDEC Solid State Technology Association announced the publication of Release 4 of the DDR3 Serial Presence
Detect (SPD) document on September 1, 2011.[27]
Intel Corporation officially introduced the eXtreme Memory Profile (XMP) Specification on March 23, 2007 to
enable enthusiast performance extensions to the traditional JEDEC SPD specifications for DDR3 SDRAM.[28]
In addition to bandwidth designations (e.g. DDR3-800D), and capacity variants, modules can be one of the following:
1. ECC memory, which has an extra data byte lane used for correcting minor errors and detecting major
errors for better reliability. Modules with ECC are identified by an additional ECC or E in their
designation. For example: "PC3-6400 ECC", or PC3-8500E.[29]
2. Registered or buffered memory, which improves signal integrity (and hence potentially clock rates and
physical slot capacity) by electrically buffering the signals with a register, at a cost of an extra clock of
increased latency. Those modules are identified by an additional R in their designation, for example
PC3-6400R.[30]
3. Non-registered (a.k.a. "unbuffered") RAM may be identified by an additional U in the designation.[30]
4. Fully buffered modules, which are designated by F or FB and do not have the same notch position as
other classes. Fully buffered modules cannot be used with motherboards that are made for registered
modules, and the different notch position physically prevents their insertion.
5. Load reduced modules, which are designated by LR and are similar to registered/buffered memory, in
a way that LRDIMM modules buffer both control and data lines while retaining the parallel nature of all
signals. As such, LRDIMM memory provides large overall maximum memory capacities, while
addressing some of the performance and power consumption issues of FB memory induced by the
required conversion between serial and parallel signal forms.
Serial presence detect
Release 4
XMP extension
Variants
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Both FBDIMM (fully buffered) and LRDIMM (load reduced) memory types are designed primarily to control the
amount of electric current flowing to and from the memory chips at any given time. They are not compatible with
registered/buffered memory, and motherboards that require them usually will not accept any other kind of memory.
The DDR3L (DDR3 Low Voltage) standard is an addendum to the JESD79-3 DDR3 Memory Device Standard
specifying low voltage devices.[31] The DDR3L standard is 1.35 V and has the label PC3L for its modules. Examples
include DDR3L‐800 (PC3L-6400), DDR3L‐1066 (PC3L-8500), DDR3L‐1333 (PC3L-10600), and DDR3L‐1600
(PC3L-12800). Memory specified to DDR3L and DDR3U specifications is compatible with the original DDR3
standard, and can run at either the lower voltage or at 1.50 V.[32] However, devices that require DDR3L explicitly,
which operate at 1.35 V, such as systems using mobile versions of fourth-generation Intel Core processors, are not
compatible with 1.50 V DDR3 memory.[33]
The DDR3U (DDR3 Ultra Low Voltage) standard is 1.25 V and has the label PC3U for its modules.[34]
JEDEC Solid State Technology Association announced the publication of JEDEC DDR3L on July 26, 2010[35] and
the DDR3U on October 2011.[36]
Introduction of asynchronous RESET pin
Support of system-level flight-time compensation
On-DIMM mirror-friendly DRAM pinout
Introduction of CWL (CAS write latency) per clock bin
On-die I/O calibration engine
READ and WRITE calibration
Dynamic ODT (On-Die-Termination) feature allows different termination values for Reads and Writes
Fly-by command/address/control bus with on-DIMM termination
High-precision calibration resistors
Are not backwards compatible—DDR3 modules do not fit into DDR2 sockets; forcing them can
damage the DIMM and/or the motherboard[37]
Higher bandwidth performance, up to 2133 MT/s standardized
Slightly improved latencies, as measured in nanoseconds
Higher performance at low power (longer battery life in laptops)
Enhanced low-power features
List of device bandwidths
Low power DDR3 SDRAM (LPDDR3)
Multi-channel memory architecture
DDR3L and DDR3U extensions
Feature summary
Components
Modules
Technological advantages over DDR2
See also
7. 02/10/2020 DDR3 SDRAM - Wikipedia
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a. Prior to revision F, the standard stated that 1.975 V was the absolute maximum DC rating.[12]
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JEDEC standard No. 79-3 (JESD79-3: DDR3 SDRAM)
DDR3 SDRAM standard JESD79-3F (https://www.jedec.org/standards-documents/docs/jesd-79-3d)
Addendum No. 1 to JESD79-3 - 1.35 V DDR3L-800, DDR3L-1066, DDR3L-1333, DDR3L-1600,
and DDR3L-1866 (JESD79-3-1A.01) (https://www.jedec.org/standards-documents/docs/jesd79-3-1
a01)
Addendum No. 2 to JESD79-3 - 1.25 V DDR3U-800, DDR3U-1066, DDR3U-1333, and DDR3U-
1600 (https://www.jedec.org/standards-documents/docs/jesd79-3-2)
Addendum No. 3 to JESD79-3 - 3D Stacked SDRAM (https://www.jedec.org/standards-documents/
docs/jesd79-3-3-0)
SPD (Serial Presence Detect), from JEDEC standard No. 21-C (JESD21C: JEDEC configurations for
solid state memories)
SPD Annex K - Serial Presence Detect (SPD) for DDR3 SDRAM Modules (SPD4_01_02_11) (htt
p://www.jedec.org/standards-documents/docs/spd-4010211)
DDR, DDR2, DDR3 memory slots testing (http://start-test.com/Products/JTAGExternalModules.php)
DDR3 Synchronous DRAM Memory (https://courses.cs.washington.edu/courses/cse467/11wi/lectures/
SDRAM.pdf)
Retrieved from "https://en.wikipedia.org/w/index.php?title=DDR3_SDRAM&oldid=980499996"
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37. "DDR3: Frequently Asked Questions" (https://web.archive.org/web/20091229190303/http://www.kingst
on.com/channelmarketingcenter/hyperx/literature/MKF_1223_DDR3_FAQ.pdf) (PDF). Archived from
the original (http://www.kingston.com/channelmarketingcenter/hyperx/literature/MKF_1223_DDR3_FA
Q.pdf) (PDF) on 2009-12-29. Retrieved 2009-08-18.
External links