CC BY
61ParaLab - Visual Way to Parallel Progarmming
The software system for investigating the parallel algorithms
Anna Labutina, Victor Gergel Nizhny Novgorod State University Nizhny Novgorod, Russia e-mail: [email protected]
In this paper we introduce a software system which allows to carry out and visualize computational experiments for studying and researching the parallel algorithms of solving complicated computational problems in imitation mode on one single sequential computer. User can “assemble” a parallel computational system of cluster type that consists of multiprocessor and multicore nodes connected with the network, set up the problem to be solved, carry out the parallel solving algorithm, collect and analyze the results of computational experiments. To estimate the execution time of parallel method on current hardware system we use the sophisticated models. For every implemented parallel method we proved the theoretical estimations of the execution time by comparing the real time of the execution on the NNSU high performance cluster with the time, that can be calculated using the model.
High performance computing, parallel computing, parallel computations modeling, cluster, multiprocessor architecture, multicore architecture.
I. Introduction
The development of the computer architecture and network technologies, together with investigations of new time-consuming scientific and applied problems that demand massive computations showed high necessity of parallel computations, made high performance computing the cornerstone of programming and computational technology.
But despite of science needs and the actuality of parallel computations, so far they are not as widely used as it was predicted. One of the possible reasons is the necessity of developing new parallel algorithms to solve the new computationally intensive problems. It is well-known that the speedup of solving the task on parallel computational system can only be achieved when the algorithm is divided into set of independent processes that can be run simultaneously. The other reason is that the debugging of parallel code is a high complexity problem, which makes it necessary to fully understand the behavior of the system of computational processes run in parallel. That is why competence in modern high perforamnce computational system design trends, in new tools developed to achieve parallelism, the ability to create models, methods for solving the problems in parallel are the major qualities for specialists in applied mathematics, computer science and information technologies.
One more reason why “real” parallel programs are so hard to understand is that there is no simple tool to visualize their behavior. It is getting even worse now when unified interfaces are used to organize an access to multiprocessor computational systems. Such interfaces allow user to put the task into the queue remotely, and then after a while get the result of program execution stored in the file.
An access to real parallel computational system is not necessary for learning the basic principles of parallel algorithm’s execution. Textbooks will help to gain the sufficient level of theoretical knowledge. ParaLab system introduced in this paper will help to get the practical expertise.
II. Working with ParaLab
While working with ParaLab user has an access to a wide range of tools to set the computational experiment parameters. She can model the computational system, chose the problem, carry out the parallel algorithm, collect and analyze the results of computational experiments.
A. Modeling the Parallel Computational System
ParaLab allows to simulate the parallel computational experiments execution on multiprocessor (SMP) and multicore architectures. The computational system appears to consist of the computational nodes (computers). Each node has one or more processors, and each processor has one or more cores. The ParaLab system architecture doesn’t limit the maximum amount of cores in processor and processors in one node, but for the sake of visualization we limit the number of cores to be equal to 1, 2 or 4 and number of processors to be 1 or 2.
In order to simulate the computer system, it is necessary to determine the network topology, the number of computational nodes, the number of processors and cores on one node, the performance of each core, and the characteristics of the communication network (latency, bandwidth and data communication method). It should be noted that the computer system is assumed to be homogeneous in the ParaLab system, i.e. all the computational nodes have the equal amount of processors, every processor consists of the same number of cores, cores possess equal performance, and all the communication lines have the same characteristics.
The data communication network topology is defined by the structure of communication lines among the computer
system nodes. The system ParaLab supports the following network topologies: farm, ring, star, mesh, hypercube, full graph (clique).
Figure 1. Dialog windows to set up the computational system parameters
The system ParaLab allows user to set the desirable number of nodes for the selected topology. The choice of the system configuration is performed in accordance with the type of the topology used. Thus, for instance, the number of processors in a two-dimensional grid must be a perfect square (the sizes of the grid both vertically and horizontally are the same), while the number of the processors in a hypercube must be a power of 2.
The performance of the core in the ParaLab system is measured by the number of floating point operations per second (flops). It should be noted that to estimate the execution time of the experiment, it is assumed that all the computer instructions correspond to the same floating point operation.
The time of data transmission among the processors determines the communication overhead of the parallel algorithm execution in a multiprocessor system. The main set of parameters, which makes possible to estimate the data communication time, contains the following values:
• latency (a). It is the time, which characterizes the duration of preparing a message for transmission, as well as the duration of the searching for the route in the network, etc.;
• network bandwidth (fi). It is defined as the maximum amount of data, which can be transmitted in a certain unit of time through a data communication channel. This characteristic is measured, for instance, in Mbits per second.
Among the data communication methods, implemented in ParaLab, there are the following two well-known communication methods (see, for instance, Kumar, et al. (1994)). The first method is aimed at passing messages as indivisible information blocks (store-and-forward routing or SFR). The second communication method is based on representing the transmitted messages as a set of information blocks of smaller sizes (packets). As a result, the data transmission may be reduced to passing packets. In case of this communication method (cut-through routing or CTR) the transit processor may perform transmitting the data along the chosen route directly after the reception of the next packet without waiting for the termination of receiving all the message data.
B. Selecting the Problem and the Parallel Method
The following widely used parallel algorithms applied to solving complicated computational problems in various scientific and technical applications are implemented in the system ParaLab: the algorithms for data sorting, the algorithms for matrix operations, the algorithms for solving the systems of linear equations, graph processing, the algorithms for solving differential equations in partial derivatives and the algorithms for global multiextremal optimization.
As a rule, for every task there are several solving parallel methods implemented. For the matrix-vector multiplication task we implemented algorithms based on block, row-wise and column-wise matrix decomposition. For the matrix multiplication problem there are parallel Fox’s and Cannon’s algorithms and the algorithm based on striped matrix decomposition. For the problem of solving the system of linear equations we present the parallel variants of Gauss method and conjugate gradient method. For the sorting problem we implemented parallel variants of bubble sort, Shell sort and quick sort. For the graph processing task there are parallel algorithm for building minimal spanning tree, Dijkstra’s and Floyd’s algorithms for shortest paths problem. For the problem of solving the differential equation in partial derivatives we have parallel Gauss-Seidel algorithm. Parallel index method is implemented for the problem of multiextremal optimization.
Task properties Ua»l
Boundary condbons
^êêÉL
© Zero # Arc © Random
Start«
1 Random
CM ne
0
Gndsie 50
Catenation preasoo 001
Maximal number of iterators 300
[ ^ok._) [OCancei i*"*
Figure 2. Dialog window to set the parameters for the problem of solving differential equation in partial derivatives
The main problem parameter in the system ParaLab is the initial data amount. For the problem of sorting this is the size of the array. For the matrix operations and the problem of solving a system of linear equations this is the order of the matrices. For the problem of processing graphs this is the number of graph vertices.
User can set additional parameters for some types of problems. For example there is a possibility to chose the boundary conditions for the problem of solving the differential equation in partial derivatives, to chose the type of function for the problem of multiextremal optimization, to
create a graph with the help of built-in graph editor for the graph processing problem.
C. Carrying out the computational experiment
ParaLab provides various forms of graphical demonstration of parallel computation results in order to observe the process of carrying out a computational experiment of solving complicated time consuming computational problems. Before the parallel algorithm execution user can set the visualization parameters for demonstration speed, the mode of communication operation visualization, the required level of details to be shown.
Figure 3. The window of the computational experiment while solving the problem of matrix-vector multiplication
The system ParaLab provides different schemes of carrying out experiments to give convenient possibilities for studying and using parallel algorithms of solving complicated computational problems. Problems may be solved in the sequential execution mode, in the time sharing mode with the possibility to simultaneously observe the algorithm iterations in all the computational experiment windows. Carrying out experiment series that require long-continued computations, may take place in the automatic mode with the possibility of saving the results of solving problems in the experiment log. Experiments may be also carried out in the step-by-step mode.
D. Accumulating and Analyzing the Experiment Results
To accumulate the results of the executed experiments, ParaLab provides a special memory, which is hereinafter referred to as the experiment log. The results are stored in the experiment log by the system automatically. Accumulated results can be used for observing and analyzing. The stored data can be also taken to restore the previous state of the experiment - that allows to rerun the experiment. Also it allows to continue computations from the suspended state.
Saving the current experiment in a file, all the accumulated results will be also saved.
For the experiments saved in the experiment log, we build the graph that shows how the execution time and the speedup depend on problem and computational system parameters. These graphs are built in accordance with the theoretical models we use to estimate the execution time of the parallel algorithm.
III. Modeling parallel computations
A. Model for the local computations
While creating a model to estimate the time of local computations we assume that this time is the sum of the calculation time and the memory access time:
T1 - Tcalc + T,
(1)
Here the calculation time is the result of multiplication of the executed operations number N by the time of one operation execution t. The memory access time is the result of division of the maximum amount of data M by the memory bandwidth ftPAM. To make the estimation more precise we should consider that the data comes from memory not in byte-by-byte mode but in full cache lines, the length of one cache line is equal to L bytes. The worst case is when every data element should be downloaded from the memory and it falls in the separate cache line. Thus, the model for the local computations execution time can be the following:
Ti — N • T + L • M/Pram
(2)
We should also consider the RAM latency aRAM that can significantly influence the time of computations:
T1 — N •
T + M • (aRAM + L/ Pram )
(3)
Figure 4. The experiment log window
This model doesn’t reflect the modern processor architecture, where the processor has small but fast local memory, which is called cache memory. In order to get the fast access to the necessary data this data is downloaded from RAM to cache before the computations with the use of different prediction algorithms. This download can be performed simultaneously with computations and doesn’t affect the time of computation execution. The situation when the necessary data is not in the cache and the processor should wait for them to be downloaded from RAM is called cache miss. To make the model of computational time more precise we need to know the number of cache misses
appeared during computations. With this new information we can correct the time that the processor spends on waiting for the data to be downloaded from the RAM:
71 — N • t + y • M • (aRAM + L/Pram) (4)
where y is the cache miss ratio (number of cache misses divided by the number of cache access operations), which can be theoretically estimated.
Thus, to estimate the time of local computations execution we need to know:
• aRAM - RAM latency,
• ftRAM - RAM bandwidth,
• y - cache miss ratio,
• t - the time of one operation execution.
To make a decision about the model accuracy the computational experiments were carried out on the computer with the Intel core 2 quad Q6600 processor. The architecture of this processor includes first-level caches with the bandwidth of 153 Gb/sec and latency of 1,22 nsec. The RAM of the target system has a bandwidth of 12,4 Gb/sec and latency of 8,31-80 nsec. The algorithm of matrix-vector multiplication was executed. The code for this algorithm is:
for (i=0; i<Size; i++) {
pResult[i] = 0; for (j=0; j<Size; j++) pResult[i] +=
pMatrix[i*Size+j]*pVector[j];
}
To calculate the time of one operation execution t we measured the time spent on performing the algorithm for small object size, when matrix and vectors can fit in cache L1. We divide this time by the number of performed operations and get the time of one operation execution t = 3,78 nsec.
TABLE I. Comparison of the experimental and theoretical
EXECUTION TIME OF THE MATRIX-VECTOR MULTIPLICATION ALGORITHM
In current version of ParaLab the simplier model for estimating the time of local computation is realized. This model only uses the number of operations and the time of one operation execution t. We plan to implement the described approach to local computations time estimation in the next version of ParaLab.
B. Model for data passing operations execution
The time necessary for transmitting data between the processors defines the communication overhead of the duration of parallel algorithm execution in a multiprocessor computer system. The basic set of parameters, which can help to evaluate the data transmission time, consists of the following values:
• initializing time (a) characterizes the duration of preparing the message for transmission, the search of the route in the network etc.;
• control data transmission time (tc) between two
neighboring processors (i.e. the processors,
connected by a physical data transmission channel); to control data we may refer the message header, the error detection data block etc. ;
• transmission time of one data byte along a data
transmission channel (1/ft ); the duration of this
transmission is defined by the communication
channel bandwidth.
Let’s consider store-and- forward routing (SFR). In case of this approach the processor, which contains a message for transmission, gets all the amount of data ready for transmission, defines the processor, which should receive the data, and initializes the operation of data transmission. The processor, to which the message has been sent, first receives all the transmitted data and only then begins to send the received message further along the route. The time of data transmission tcomm for the method of transmitting the message of m bytes along the route of length l is defined by the expression:
tcomm = a + (tc + y) • 1 (5)
If the messages are long enough, the control data transmission time may be neglected, and the expression for data transmission time may be written in a simplified way:
tcomm = a + ^1 (6)
Let’s consider cut-through routing (CTR), when the
receiving processor may send the data further along the route
immediately after receiving the current packet without waiting for the termination of the whole message data transmission. The data transmission time in case of packet communication method will be defined by the following expression:
tcomm = a + ^ + tc • 1 (7)
If we compare the obtained expressions, it is possible to notice that in the majority of cases the packet communication leads to faster data transmission. Besides, this approach decreases the need for memory for storing the transmitted data. Different communication channels may be used for packet communication simultaneously. On the other hand, the implementation of the packet communication requires the
Matrix Size Experimental Time Theoretical Time Relative Error
100 0,0001 0,0001 0,0062
1000 0,0076 0,0076 0,0011
2000 0,0303 0,0304 0,0021
3000 0,0688 0,0685 0,0043
4000 0,1222 0,1217 0,0036
5000 0,1909 0,1903 0,0035
6000 0,2748 0,2740 0,0029
7000 0,3741 0,3729 0,0033
8000 0,4894 0,4872 0,0044
9000 0,6186 0,6164 0,0036
10000 0,7637 0,7611 0,0034
development of more complex hardware and software. It may also increase the overhead expenses (initialization time and control data transmission time). Deadlocks may also occur in case of packet communication.
C. The data passing operations in multiprocessor and
multicore architectures
As it was previously mentioned, in ParaLab the computational system consists of computational nodes, the network links between them are determined by the topology (farm, ring, etc.). Every node has one or more processors, every processor consists of one or more cores. We assume that the internal links between cores in frame of one computer (busses) form the full graph topology.
To make the time estimation model easier we assume that the computations and data passing operations cannot overlap, which means that the computations stop when the cores are performing the data transmission, and vice versa.
Every collective data passing operation between cores can be divided into 3 stages:
1) Data transmission between cores in frames of one computational node and sending the data into the external network (via network adapters),
2) Data transmission between different computational nodes through the local network (in ParaLab if all the data was sent to the network from one core then it is visualized like one envelope, if the data was sent by different cores it is shown like passing the pile of envelopes),
3) Receiving the data from the network adapter by the different cores in frames of one computational node.
To estimate the time spent on data passing operation we need to know:
• the size of one data unit in bytes,
• the number of units being transmitted for every pair of cores that perform the data passing operation on the current iteration of the algorithm.
To calculate the final time of the communication operation we only take into account the time of the second stage (passing the data through local network). The time spent on transmitting the data through the bus is 3 to 4 degrees less than that.
D. An Example of Computational Experiment Time
Estimation
Let’s consider the complexity of the parallel algorithm for matrix-vector multiplication based on rowwise matrix decomposition. Every core performs the multiplication of the matrix stripe by the vector, each stripe has n/p rows, where n is the size of the matrix and p is number of cores. One scalar product of the matrix row and a vector involves n multiplications and (n-1) additions. Let’s assume that the multiplication and addition have the same duration t. Besides, let us assume that the computer system is homogeneous, i.e. all the processors of the system have the same performance. With regard to the introduced assumptions, the computation time of the parallel algorithm is:
Tp (calc) = [n/pl • (2n - 1) • t (8)
The ‘all gather’ operation is used to put the result vector on all the processes of the parallel program. This operation can be performed in |7og2 p] iterations. At the first iteration the interacting pairs of processors exchange messages of size wfn/pl bytes (w is the size of one element of the vector in bytes). At the second iteration the size becomes doubled and is equal to 2w[n/pl etc. As a result, the all gather operation execution time when the Hockney [2] model is used can be represented as:
tp (romm) = £ 1í—0lg2pl ^ a + —^
= a[log2p'| + w [n/p"|(21log2pl - 1) /p
where a is the latency of data communication network, ft is the network bandwidth. Thus, the total time of parallel algorithm execution is
Tp = ^ (2n - 1 )t + alog2p + w<n/p;(p-1) (10)
(to simplify the expression (9) it was assumed that the values n/p and /og2p are whole numbers).
Let us analyze the results of the computational experiments carried out in order to estimate the efficiency of the discussed parallel algorithm of matrix-vector multiplication. Besides, the obtained results will be used for the comparison of the theoretical estimations and experimental values of the computation time. Thus, the accuracy of the obtained analytical relations will be checked.
The experiments were carried out on the computational cluster on the basis of the processors Intel XEON 4 EM64T, 3000 Mhz and the network Gigabit Ethernet under OS Microsoft Windows Server 2003 Standard x64 Edition.
The comparison of the experiment execution time 7^*and the theoretical time Tp calculated in accordance with the expression (10), is shown in Table 2.
TABLE II. The comparison of the experimental and
THEORETICAL EXECUTION TIME FOR PARALLEL ALGORITHM OF MATRIX-VECTOR MULTIPLICATION BASED ON ROWWISE MATRIX DECOMPOSITION
Matrix Size 2 processors 4 processors 8 processors
T ‘p TP T ‘p TP T ‘p TP
1000 0,0069 0,0021 0,0108 0,0017 0,0152 0,0175
2000 0,0132 0,0084 0,014 0,0047 0,0169 0,0032
3000 0,0235 0,0185 0,0193 0,0097 0,0196 0,0059
4000 0,0379 0,0381 0,0265 0,0188 0,0233 0,0244
5000 0,0565 0,0574 0,0359 0,0314 0,028 0,015
Now let us describe the way the parameters of the theoretical dependencies (values t, w, a, ft) were evaluated. To estimate the duration t of the basic scalar computational operation, we solved the problem of matrix-vector multiplication using the sequential algorithm. The computation time obtained by this method was divided into the total number of the operations performed. As a result of
the experiments the value of t was equal to 1.93 nsec. The experiments carried out in order to determine the data communication network parameters demonstrated the value of latency a and bandwidth ft correspondingly 47 msec and 53.29 Mbyte/sec. All the computations were performed over the numerical values of the double type, i.e. the value w is equal to 8 bytes.
IV. Conclusion
The Parallel Laboratory software system (ParaLab) provides the possibility of carrying out computational experiments for studying and investigating the parallel algorithms of solving complicated computational problems. The system may be used for organizing a set of laboratory works on various courses in the area of parallel programming. This laboratory works will allow the learners to do the following:
• to model multiprocessor systems with various data communication network topologies,
• to obtain the visual presentations of the computational processes and data communication operations which take place in case of parallel solving various problems,
• to construct the efficiency estimations of the parallel methods to be studied.
In general, ParaLab is the integrated environment for studying and investigating the parallel algorithms of solving complicated computational problems. A wide set of available means to visualize the process of carrying out an experiment and to analyze the obtained results allows to study the parallel method efficiency on various computer systems, to make conclusions concerning the scalability of the algorithms and to determine the possible parallel computation speedup.
The processes of study and research realized by ParaLab are aimed at mastering the fundamentals of parallel computation theory. They allow the leaners to form the basic concepts of the models and methods of parallel computations through observation, comparison and analysis of various visual graphic forms demonstrated in the course of the experiment execution.
ParaLab is mainly used for training purposes. It may be used by University professors and students for teaching/studying and investigating parallel algorithms of solving complicated computational problems using the set of the laboratory works, applied to various courses in the area of parallel programming. ParaLab may be also used to conduct research into estimating the efficiency of parallel computations.
For those who only start to study the problem of parallel computations, ParaLab is very useful, as it allows them to master the parallel programming methods. Experienced users may use the system in order to estimate the efficiency of new parallel algorithms, which are being developed.
[1] Foster, I. Designing and Building Parallel Programs: Concepts and Tools for Software Engineering. Reading, MA: Addison-Wesley (1995).
[2] Hockney, R. W., Jesshope, C.R. Parallel Computers 2. Architecture, Programming and Algorithms. - Adam Hilger, Bristol and Philadelphia (1988).
[3] Kumar V., Grama A., Gupta A., Karypis G. Introduction to Parallel Computing. - The Benjamin/Cummings Publishing Company, Inc., (1994).
[4] Quinn, M. J. Parallel Programming in C with MPI and OpenMP. -New York, NY: McGraw-Hill (2004).
[5] Rajkumar Buyya. High Performance Cluster Computing. Volume 1:
Architectures and Systems. Volume 2: Programming and
Applications. Prentice Hall PTR, Prentice-Hall Inc. (1999).
[6] Xu, Z., Hwang, K. Scalable Parallel Computing Technology, Architecture, Programming. - Boston: McGraw-Hill (1998).
[7] Voevodin V.V., Voevodin Vl.V. Parallel Computations. Saint-Petersburg, BHV (2002).
[8] Gergel V.P. Theory and Practice of parallel computations. BINOM (2007).
[9] Korneev V.V. Parallel Computational Systems. - Moscow, Knowledge (1999).
[10] Tanenbaum E. Computer Architecture. - Saint-Petersburg, Piter (2002).
References
