Computer Simulation is a fundamental tool for scientific research and the condensed matter sector in SISSA has a long and outstanding experience in the field of numerical simulations. The rapid growth of computer power, including the new parallel architectures, allows to deal nowadays with computational problems that were judged not even affordable only a few years ago. The computational techniques currently used can be classified in three main classes:
The relative obsolescence of our computer resources recently motivated us to test different parallel platforms in order to understand which is the hardware solutions that can fit at best our computational requirements. For this reason a set of representative codes was set-up and by means of them we are currently benchmarking several parallel architectures. Along this line we started to build a parallel machine based on SMP Intel nodes connected by high-speed networks. A small prototype (4 node with 2 processor each) is now currently under exhaustive test of our scientific computational requirements. The goal is to define which class of computational tasks this type of machines can solve with the best performance/price ratio. The aim of this paper is to report the work done in this direction.
The paper is organized in the following way: section 2 describes the set of programs we used to test our parallel platforms. In section 3 we present some technical details of our cluster implementation mainly discussing the network interconnections and their performances. A detailed analysis of the performances of the codes is presented and commented in section 4. Some conclusions will be drawn in Section 5.
In this section we describe the representative suite of parallel codes employed in this study. As already pointed out the three main classes of applications used by our researchers have distinct characteristics from the computational point of view.
Ab-initio codes are computationally demanding in term of both memory and CPU time. Parallel versions of electronic structure codes have been already developed in the past years using the Message Passing (MP) paradigm. In this way parallel codes could exploit at maximum the modern massively parallel computers (MPP). Due to this effort, highly scalable programs, showing an almost linear speed-up over a wide range of processors (64/128) are now routinely used. We include in our suite two parallel codes belonging to this class, both of them developed at Sissa: PWSCF and FPMD.
Classical Molecular Dynamics codes has different computational characteristics: the memory requirements are lower and the scalability is limited over a small number of processors. For this reason such codes work better on shared memory machines which are the target machines of the locally developed codes. The benchmark suite includes two parallel codes of this type: DLPROTEIN_2.0 and Amber-5.0
The codes implementing Quantum Monte Carlo techniques do not need too much dynamical memory and can be run efficiently on relatively small machines where each processor works on its own task and communications do not represent a bottle neck. The parallel code gf3z is the representative QMC code included in the suite.
In the following a short description of each code is given together with some details about the computational load and the parallelization aspects.
The main computational platform is the Cineca T3E and for this reason a careful analysis of the performances and subsequent optimizations for that platform has been carried out .
In the following we briefly outline the computational load of the program with respect with the size of the inputs. We refer to reference for further technical details about the code.
In the code the wavefunctions are represented in a Plane Waves (PW) basis in the reciprocal space and the number of PW's Npw is one of the main parameters which define the computational load of the code. The code makes large use of Fast Fourier-Transform (FFT) algorithms to transform wavefunctions from the reciprocal space to the direct space. Other quantities defined on a grid with dimensions strictly related to Npw are FFT transformed in the same way. The number of operations related with Npw is then proportional to Npw ×lnNpw
The most time consuming step of the program is the computations of the wavefunctions by means of an iterative procedure (the Self Consistent Field: SCF). This task is accomplished by two methods: a Conjugate Gradient (CG) routine, or a Davidson algorithm. There are two other parameters, Nb, number of bands of the system and Nprojector, number of projectors needed to describe the nonlocal pseudopotential, which contribute to the computational load of SCF procedure.
The CG method requires several applications of FFT procedures and a large number of vector-vector operations. The size of vectors is proportional to Nb x Npw. These linear algebra operations are accomplished by means of BLAS1 routines.
The Davidson method is generally more efficient and is the most often used. The computational aspects can be summarized as follows:
The parallel implementation is achieved with an even distribution of the PW among the processors (PE). Each PE owns an effective number of plane waves approximately equal to the others. The FFT grid is also split among them. Both the CG algorithm and the Davidson procedure exploit the PW distribution and almost all the tasks required by these procedures are done in parallel. The linear algebra kernels scale linear because the data size is split among processors but a certain communication overhead is introduced to collect data. These communication operations are mainly global operations (reduce gather/scatter) on specific pools of processors. Concerning FFT the code implements ``ad hoc'' parallel FFT algorithms to exploit at the best the underlying structure of the distributed data. The matrix diagonalization is done in parallel only if the size of the matrix involved is larger than a threshold value. The code scales well over a wide range of processors (16-64) on T3E. Systems currently under study require a very large amount of memory and the number of T3E processors needed for these runs is in the 32-64 range.
FPMD is a modular parallel code implementing the Car-Parrinello algorithm including the variable cell dynamics. The code is developed by C.Cavazzoni and G.L.Chiarotti: it is written in F90 and makes use of some new programming concepts like encapsulation, data abstraction and data hiding. The code has been recently used to simulate water and ammonia at giant planet conditions. The computational cost and the parallelization strategy of the code are very similar in many aspects to the previous PWSCF program and here we limit to report in table the main computational tasks and the communication algorithms with the relative cost. We refer to reference for a full description of the code.
|Matrix-vector operations||Nb2 ×Npw|
|FFT transforms||Npw ×ln Npw|
|All to All||Nb ×Npw ×ln Npw|
|Global reduce||Nb2 ×ln NPE + Nat* Nb ×ln(NPE)|
|Gather and scatter||Npw ×Nb|
DLPROTEIN_2.0 is a Molecular Dynamics package written by S. Melchionna and S. Cozzini and it is a development of the original general purpose Molecular Dynamics code DL_POLY_2.0  written at Daresbury Lab (UK) by W. Smith and T. R. Forester. The motivation underlying the development of DLPROTEIN_2.0 has been to create a simulation package well suited for biological molecules, with particular focus on proteins, with high efficiency and with a programming language and style suitable for complex molecular topologies. DLPROTEIN_2.0 is a a package consisting of two major parts, a topology builder for (bio)molecules and a molecular dynamics (MD) engine. DLPROTEIN_2.0 allows to simulate systems in different thermodynamic ensembles (NVT,NPT,NHT) by means of time reversible algorithms and implements modern methods to reduce the computational cost of the simulation.
Classic MD conceptually is quite simple: it is an iterative process where at each step the sum of all forces on each atom is calculated and then applied, updating the position and the velocity values. The forces originate from bonded and non-bonded forces between atoms. A single atom has bonded-related force with a limited number of other atoms, while the non-bonded forces exist between all pairs of atoms yielding a o(Na2) interactions (Na being the number of atoms). The non-bonded forces are comprised of electrostatic forces (long-range) forces and Lennard-Jones forces (short range forces) and there are different tricks to deal with them. Short range forces are computed by means of the neighbor list technique. Each particle just interacts with particles within a certain cutoff distance (the neighbors) and these atoms are stored in a list. The list is nothing but an array of pointers to the position arrays. This means that the data are loaded in a very scattered way with bad performance of the cache mechanism. The amount of computation required is proportional to Na (with a large proportionality constant: the number of neighbors: Nneigh). Long Range forces are treated by means of the Smooth Particle Mesh Ewald (SPME) algorithm that requires FFT kernels on a 3D grid. The size of the grid is defined by the Na, number of atoms in the system. The non bonded computations (short range + long range) constitute between the 80 and 95 percent of the overall computations, depending on the systems. Table summarizes the main tasks of an MD program and the algorithms implemented in DLPROTEIN_2.0.
|Short Range Forces||Link-cell + neighbor lists||Nneigh ×Na|
|Long Range Forces||Smooth Particle-Mesh Ewald (SPME)||Na ×ln Na|
|Bonded Forces comp.:||2-body,3-body,4-body potential||negligible|
|Updating Atoms||velocity verlet algorithms||negligible|
The parallelization strategy adopted by DLPROTEIN_2.0 is based on the Replicated Data (RD) approach implemented using Message Passing technique. All data (arrays containing attributes, coordinates and forces of atoms) are replicated on each processor. The force computations can then be evenly distributed among processors at will, as any processor is capable of carrying out any particular force computation. If there are Na atoms and PE processors the O(Na/PE) forces accumulated by each processor must be added up across all processors. This requires a reduce operation with a communication time proportional to Na ×ln PE . A similar operation is required to update positions among processors as each of them just moves Na/PE particles. The ratio among communication time and computation time is therefore given by PE ×ln PE and it is independent of Na. So if we want to simulate a system twice as large as the current one, we cannot hope to double the number of processors to retain the same efficiency, because the time spent in communication will be larger. Thus RD is non scalable.
It is important to note however that in practice RD works effectively the range 1-8 and there is no real need of large MPP platforms to run this kind of code; memory requirements, even for large systems are limited and can be easily satisfied by small SMP machines.
The Amber-5.0 package is another MD code heavily used for simulation of proteins here at Sissa. Several users are currently running very large systems with the MD engine of the package and for this reason we include this program in our benchmark suite.
The computational characteristics of the code are practically the same of DLPROTEIN_2.0; also the parallelization strategy is the same as before,i.e. Replicated Data. There is however a significant difference in its implementation for the SGI Origin-2000 version of the code: on this platform the code is parallelized using a Shared Memory approach. This means that the overhead due to communication in Message Passing implementation could be greatly reduced and the scalability of the code improved. The parallelization has been done using specific Origin-2000 directives and not using a standard like openMP. The Shared Memory version is therefore specific for that architecture while for other platform a standard MPI version is available. We compiled the code on the Beowulf cluster using the MPI version.
The QMC code included in the suite has been developed by S. Fantoni and coworkers and applies a particular formulation of the QMC techniques to a systems of Nucleons.
This formulation allows to sample statistically the ground state of a many-body Hamiltonian by a set of ``walkers''. By statistical iterations of a very large number of walkers the ground state of the system could be estimated.
The computational load is related to the number of particles which compose the system: this number defines the size of the ``walkers'' vectors and the size of Hamiltonian matrix diagonalized at each iteration. The sizes of the quantum systems numerically investigated are not very large and therefore the diagonalization procedure is not computationally dominant.
The code can easily be parallelized because an even number of walker can be assigned at each processor at the beginning and the simulation. Each processor can then run independently. It is necessary however after several iteration to check the number of walkers on each processor and balance the load among the processors because the number of walkers does not remain constant as the simulation proceeds. The load of this operation is anyway negligible for this specific case and the code scales linearly over the all range (1-256) on the T3E machines.
As final points of this section we want to compare the three families of codes looking at specific aspects of high performance computing (HPC). Table presents such a comparison. The three columns represents the three classes of codes we discussed above while each row indicates a peculiar characteristic of the HPC. An adjective defines the behavior of each class on that specific subject. From the table emerges clearly that the computational requirements of the three classes are quite different.
|aspect||Ab-initio||Classical MD||Quantum MC|
|Parallelization strategy||MPI||MPI/Shared Memory||MPI|
|Use of Linear Algebra kernel||high||almost null||moderate|
On the computing nodes there are 2 Pentium III (Katmai) processors running at 550 Mhz. These processors have an on chip 16 KB + 16 KB separate instruction and data level 1 cache and an off chip (but in-package) discrete 512 KB level 2 cache on a separate bus (Back Side Bus) at f/2 Mhz.
The processor bus or Front Side Bus runs at 100 Mhz. On the Front Side Bus, the Intel 440 chipset manages the memory and the peripheral buses(a 32 bits @33 Mhz PCI). Memories are commodity 100 Mhz SDRAM. Each computing node has 256 MB of memory. A schematic view of the machine is presented in fig 1.
Because of the requirement to frequently switch kernels, drivers and software setup, since the beginning we planned to reduce at a minimum the system management overhead. For this reason we installed netboot eproms on the Fast Ethernet cards and all the computing nodes access their root and usr partition on the service node via NFS. The nodes are now running a linux 2.2.14 smp-enabled kernel. Computing nodes have a local disk that is mainly used as a temporary scratch area and swap disk. We chose to install PBS (Portable Batch System) as our batch system. We mainly use the fortran compilers available from the Portland Group (PGI).
The service network - a Fast Ethernet - supports the remote boot, common file systems through NFS, remote logins, etc. We have used for the service network 3COM 3C905B cards and an Allied Telesyn switch. For this network the most strict requirement was the possibility to have netboot eproms on the cards. We used it in some comparison as a reference.
The aim of the cluster was the characterization of the performance for multiple interconnects and software combinations. As high performance interconnect we installed Myrinet and Gigabit Ethernet.
The quality of communication software has a great weight in the overall performance of a cluster. Various protocols are available (Link-Layer, VIA, TCP/IP, GM, BIP) and we are currently testing all of them. The most stable among them is the GM protocol and this protocol is the one we used to test the machine against our suite of code. GM is a lightweight communication system for Myrinet that supports reliable ordered delivery and protected access, provided directly by Myricom. It requires the use of DMAable memory (registered with the system or allocated through the system). It supports messages up to 231-1 bytes if the OS allows such amount of DMAable memory. It has 2 levels of message priority to help avoiding deadlocks in an efficient way . In the GM programming model a reliable connection is established between hosts, while communication endpoints do not need any communication establishment to communicate(connectionless). It supports up to 10000 nodes. Sends and receives are regulated by implicit tokens representing space allocated to the client by the system in its queues.
We report the performances of the various network protocols indicated before We used BIP 0.99, GM 1.2 and MPICH 1.2.0 both over TCP and over GM. During the tests a considerable difference in the TCP latency time has been observed enabling/disabling the SMP capability of the Linux kernel: this is due to the overhead in locking/unlocking of the internal structures to assure mutually exclusive access in case of multiple CPU.
Table reports the different values of latency measured. Latency time over Myrinet is comprehensive of the switch delay while in the case of fast Ethernet both values were measured. The high value for gigabit Ethernet (using a direct link) is due to the use of the interrupt coalescing feature of the NIC to assure an high b/w.
The best bandwidth obtained on the cluster are presented in figure .
At the MPI level we measures performances by means of the PALLAS benchmark suite MPI2. The figures and show the bandwidth obtained on a ping-pong and times measured for a reduce operation using 8 processors. If available we inserted data for T3E and ORIGIN 2000. It has to be noted in fig the bad performances in the range 10K-100K on the myrinet network.
In this section we present the results obtained running our codes on three parallel machines: T3E and Origin-2000 installed at Cineca and our local Beowulf cluster. Table provides a short comparison of three platforms. A rough evaluation of prices is included: we report the cost of our 32 PE quota on Cineca T3E and the cost of our local 16-node ORIGIN-2000.
|System &processor||Beowulf||CrayT3E||Origin 2000 (O2K)|
|processor||Intel PIII 550 MHz||Alpha 21164 600Mhz||R12000 300Mhz|
|N of processors||8||256||64|
|DRAM (total)||1Gbyte||49 Gbyte||32Gbyte|
|Primary cache(data)||16Kbyte||8 Kbyte||512 Kbyte|
|Secondary cache||512 Kbyte||96 Kbyte||8 Mbytes|
|Kind||Myrinet switch||Torus 3-D||Fat Bristled Cube|
|Bandwidth(Pallas)||90 Mbyte/sec||180 Mbyte/sec||100 Mbyte/sec|
|Latency(Pallas)||15 microS||13 microS||12|
|O.S||Linux 2.2.14smp||Unicos/Mk 2.0.4||IRIX 6.5.8f|
|Compilers||PGI 3.0.3||Cray Compilers 22.214.171.124||MIPS Pro 7.30|
|Comm. Library||MPICH over GM||Cray libraries||SGI libraries|
|Costs||» 25,000$||» 20 ×||» 10×|
For each code a certain number of tests was prepared and then run on all the three parallel machines. Execution times were collected using the internal timing system of each code. Tests are repeated several times in order to estimate the error that is generally less than 2% on dedicated machines like T3E and the Beowulf clusters. Origin-2000 machine is a shared machine and therefore times could be quite different when we run in parallel. In order to minimize this ``sharing effect'' we monitor the load of the machine and we tried to run our tests when at least the double of the requested processors were free. This precaution seems to work for 2/4 processors runs (error bar is » 2-3 % ) while for 8 processors the differences between runs remain still large in some cases ( » 10-15% ).
For QMC and classic MD codes we measure performances on tests that are actual systems under study. In this way the comparison among parallel platforms is complete and significant for our scientific computational requirements. In the case of ab-initio codes this was not possible: the small configuration of our cluster does not permit to run large systems but just to test specific ``ad hoc'' examples and some medium size examples.
We consider three different tests for this program. The first two examples are similar: a full relaxation toward the minimum energy configuration of two small molecules (CO and O2) is performed. The self-consistency procedure (SCF) is computed several time using the Davidson procedure for both the examples. These two tests are very small examples that can fit on single processor allowing to estimate the computational efficiency of the code on different kind of processors.
The third test is a C60 molecule. For this case we perform only a single step of the self-consistency procedure. The step was done using the two procedures the code offers: Conjugated Gradients and Davidson. This test is a medium size example and it is the largest one that can be successfully run on our small Beowulf machine. The system is described by a small number of plane waves but with a relatively large number of bands and projectors: it follows that the linear algebra kernels in the Davidson procedure (Diagonalization and BLAS2/3 routines) are dominant compared to FFT kernels. We run the test using 8 PE on all the architectures.
The sizes of parameters for all the tests are reported in table .
|Parameter||CO molecule||O2 molecule||C60 molecule|
First we discuss the behavior of the small two tests with the help of table where the total time and the time spent by the code in doing FFTs are reported. Linear algebra load is marginal compared to the FFT cost in SCF procedure and it is therefore not reported.
The R12000 processor is the fast processor but the behavior of the code on the Pentium III machine is quite promising with respect to the RISC 12000. The relatively old Pentium 550Mhz is just twice slower than the RISC 12000. Our hope is that this factor can easily reduced using Pentium III processors available now (733 /800 MHz). The other positive aspect is the good efficiency of the FFT procedures on the INTEL architecture: the public FFTW library we currently use is the code works fine and compares well with highly optimized vendor libraries. Again we hope that with other powerful INTEL processors the gap in performances will eventually disappears.
We now discuss the parallel behavior by means of the third example; data here are reported just for T3E and Beowulf cluster since on the ORIGIN-2000 at Cineca, this large example cannot be correctly timed due to the fact the ``sharing effect'' was quite pronounced. We use LAPACK/BLAS library generated by means of the ATLAS project on the Beowulf cluster because the precompiled pgi versions of these libraries are very inefficient.
Table refers to the Conjugate Gradient algorithm. There is an overall factor of less than two in performances among the two architectures; we report global times for FFT procedure and specific sections of the code where BLAS1 routines are used. The ratio in performances on these different parts of the code ranges from 1.65 to 2.35. This means that on these main sections of the code the gap in performances between the two architectures is similar.
It has to be noted however that on the INTEL processors the efficiency of the BLAS1 routines in a particular section (add_1vupsi) is 2.35 times slower than on the Alpha. This indicates that for BLAS1 routines there is still a large gap among INTEL and RISC processors.
We discuss now the Davidson procedure (table ) where diagonalization procedure and BLAS2/3 routines play a major role. The overall factor in this case is 2.16. Looking at specific sections of the code the situation is similar to the previous case with just an exception: the diagonalization procedure; this bad results is because this procedure is done in scalar on the Beowulf Machine: the parallel diagonalizer used on the T3E is still to be ported on this architecture.
We can turn now on network performances for the parallel runs. It is interesting to note the behavior of the FFT routines. Performances obtained by the scalar version of the FFT procedure are practically the same on Beowulf and T3E. The situation changes for the parallel case: on the Beowulf cluster parallel FFTs run approx. 1.6 slower than on T3E. The communication overhead of the Beowulf is therefore 1.6 times than on T3E: comparing this result with the bandwidth ratio measured by the Pallas benchmarks ( » 2) (see table 5) one could infer that in the case of FFT point-to point communication the actual performances of the Myrinet network are excellent.
A final note concerns the network performances of the reduce operations used on C60 simulations. Data are collected in the following table:
|system||Beowulf||T3E||ratio||N of calls|
There is a very large number of reduce operations in CG procedure, each of them costing little, and from this point of view this could measure a kind of ``code reduce'' latency over the network. On the contrary the limited number of the time consuming reduce operation in the Davidson procedure can measure the ``reduce'' bandwidth available to our code. The ratio between platforms of the ``code reduce'' latency (2.69) is quite similar to the same quantity estimated using Pallas MPI benchmark results. In the case of the ``code reduce bandwidth'' the high ratio between values obtained on the two platforms seems to indicate that the average size of the data involved in the operations is in a range where performances are quite bad for myrinet (see fig 4).
FPMD code comes with a benchmarking test composed by a small system of 32 water molecules. We therefore run this test on our cluster and the data obtained are then compared with performance data provided with the code . The size of the parameter set is the following: Nb = 128 , Npw = 14000 giving a FFT of 72x72x48 and Nat = 96. This small test fit an a single processor and can be used to test parallel performances over small number of processors. The following tables compares the performances obtained on the three architecture over the range of processors we can use.
The comparison among processors shows that the code is more than 2 times slower than T3E on single processor. This result is in agreement with parallel performances data collected for PWSCF code. Figure gives a graphical idea of the overall scalability of the code on the three different architectures.
The super-linear scalability on the T3E is mainly due to cache effect: the reduction in size of local arrays could reduce also the cache miss events, exploiting at best the cache mechanism. The code scales well also on the ORIGIN2000 machines with a bad speed-up just for the 4 processor case.
The behavior on the Beowulf cluster is not so good; speed-up is disappointing for 2 and 4 processors while is slightly better for 8 processors. The communication overhead is larger than the expected one. We guess that reduce operations are working on data sizes not favorable for myrinet as pointed out in the previous section.
DLPROTEIN_2.0 performances are measured on three tests. The first test is a standard benchmark of the code: a system composed by 4096 water molecules that allows to test specific algorithms to deal with water molecules. Test 2 refers to a system composed by two identical proteins (1347 atoms each) solvated with 5494 water molecules. The system is currently under study at the University of Roma. The third test is a system containing 45 Micelle (each of one formed by 80 atoms) solvated with 8513 water molecules. System 2 and 3 have an high degree of connectivity with a large number of constraints. Test 1 one is different from this point of view: it only has water constraints. The values of the significant parameters of the tests are collected in table .
|Parameter||Test 1||Test 2||Test 3|
|N of waters||4096||5494||8513|
|Nconstraints||12288||1117×2 + 5494 × 3||45× 105 + 8513× 3|
|averaged Nneigh||» 47||» 200||» 50|
We present and discuss performance data in two separate section: the single processor performances and the parallel performances.
Table collects scalar performances for the three tests. We report for each test the global time and time spent in the most consuming tasks of the code. Times are referred to single iteration. The short range computation is split here in the two subtasks: building the neighbor list and the actual computation of short ranges using the list. The two subtasks have different computational characteristics: the first is based on integer operations and data are accessed in direct way, while in the second one floating point operations are dominant and data are accessed through the list.
There are several observations that can be made:
|Built Link List||0.57||2.72||0.94|
|Long Range (SPME)||0.33||0.42||0.24|
|Built Link List||0.55||1.43||0.72|
|Long Range (SPME)||2.99||2.87||1.50|
|Built Link List||0.90||2.50||1.23|
|Long Range (SPME)||7.98||8.02||4.40|
Parallel performances of DLPROTEIN_2.0 are reported for all the three parallel machines. Figure spots out the overall scalability for the three test cases in the range 1-8 processors; parallel behavior is practically the same for Origin-2000 and Beowulf Cluster: the better scalability of the T3E with respect to the other machines is due to a combined effect of two opposite factors: the lower performances of the T3E processor and the excellent behavior of network.
This observation is easily confirmed by looking at ratio between communication time and total computational time as reported in table ; for the three examples total time, total communication time and percentage between the two are reported.
Communication/computation ratio is always lower than 10% on T3E machine while on the other two platforms the percentage of time spent in communication can reach almost 40%. It has to be noted however that parallel global times obtained on T3E are still greater than Beowulf times, indicating that the excellent network performances are not enough to counter balance the bad scalar performance of the T3E processor.
The communication time is given by the sum of all the different communication operations in DLPROTEIN_2.0 during the run. The most time consuming ones are the reduce operations (gdsum) that reduces the force and the velocities values at each time-step and all_gather (merge) where new positions are updated. Performances of these two operations obtained on Test 3 are reported in the following table:
The T3E network shows excellent performances: by increasing the number of processors the time slightly increases for the reduce operation but practically remains constant for the merge one. For the other two networks the time grows as the number of processor increases. Reduce operation follows quite close the expected logarithmic law while for the merge operation this behavior is not so evident either for O2K or for the Beowulf network. A comparison can be done between myrinet network and Origin-2000 network: the ratio between times for reduce operation is around 2, while for the merge operation is something less. These ratios are similar to the ratios obtained for computation on single node. As a consequence of this we can image that an increasing of computational power of the scalar INTEL processor could cause a decreasing of the overall scalability for this particular kind of codes because the communication time remains constant and weights more and more. This could be a limiting factor for the Beowulf platform.
The system we tested using Amber packages is real system currently under investigation here at Sissa formed by a protein (the NGF-TrkA ligand-receptor complex) solvated with 13025 waters for a total of 45717 atoms. Performances are measured just for Origin-2000 and Beowulf Cluster: our goal is to compare performances of codes which use different parallel strategy on the two architectures. Results obtained on Beowulf machine are stable: the error bar is less than 2% while for Origin-2000 the situation is again under the ``sharing effects'' already explained. Using the shared memory version of the code we noted that ``sharing effect'' is maximum for 8 processors runs showing differences in time between tests of 15 percent. Data collected for Origin-2000 are not fully feasible in terms of absolute values but still deserve to be compared with Beowulf data. This comparison is done on a system that is currently simulated on the Origin-2000 resource in those precise conditions: a shared resource with a high computational load. Data are presented in the following table:
Scalability results resemble those of DLPROTEIN_2.0 even for the Shared Memory version of the code. The ratio in performances among Beowulf cluster and R12000 is practically constant over the 1-4 range. The 8 processors datum is affected by the problem discussed but still is not far from the other values.
The negative aspect of the results is the limited scalability of both the platforms. This is quite surprising in the case on the Origin-2000 where the different Shared memory parallel implementation seems not to outperform the MPI one as one could expect. To check better this aspect we run a specific benchmark of Amber (4096 H20 water) on the Sissa Origin-2000; this machine is equipped with R10000 processors so performance on single node are lower but we could run the tests in dedicated way. Table summarized data for this test obtained on both Origin-2000 and Beowulf cluster.
The overall scalability of this benchmark is slightly better than that of the previous case but still the behavior of two implementations looks quite similar with no significant differences.
The gf3z code is tested here just for T3E and the Beowulf cluster because these two platforms are in this moment the production platforms. The test we run is just a slice of the large production runs: the code is run in parallel on the full Beowulf machine and on 32/16 T3E processors; the scalability is linear on both machines: communication overhead is practically negligible in both cases. Here we report just the relative performances of two platforms on a single processor to compare the behavior of the code: the Pentium processor is » 5% slower than the Alpha processor. We performed a parallel run using 8 processors on both machines obtaining practically the same ratio in performances.
To summarize all our results discussed before we report in table a short overview of the performances obtained by all the codes on the different machines; for each code we define two benchmark values: the first, named Scalar Benchmark (SB) is given by summing times over all the serial runs. The second, named Parallel Benchmark (PB), is similar but the summed value refers to 8 processors runs.
|code||SCALAR (SB)||PARALLEL (PB)|
These benchmark values help to draw some observations about the overall behavior of different classes of codes on the parallel platform we tested.
We consider first ab-initio codes; for this group the T3E machine is the actual production machine and we look at performances of the Beowulf machine with respect to this one. Scalar performances between T3E and Beowulf cluster (SBbeowulf/SBt3e = 1.35 - 2.12) are better than the parallel ones (SBbeowulf/SBt3e = 1.99 - 2.67 ). We can not test at this moment parallel performances on larger configurations (16-32 nodes) that could allow to run real size systems but it seems that the Beowulf network will be unable to cope with large number of nodes. Therefore it could be rather difficult to run real simulation with the network technology at our disposal in this moment. It has noted however that increasing the scalar performances by means of more recent INTEL CPU, could reduce the need of large number of processors allowing to run at least medium size system in a range where the network behavior is still acceptable. This makes the Beowulf cluster a valid support platform where to run small/medium size applications.
Concerning Classical MD codes the target machine is Origin-2000: the ratio in performances between Beowulf cluster and Origin-2000 is 1.65 -1.83 and maintains almost the same values also for the parallel case; this is a good result that makes the Beowulf machine a interesting computational resource for this kind of code with a excellent performance/price ratio.
Scalar and parallel performances obtained with the QMC code are the same due to the embarrassing parallelism of the code itself. The overall performances obtained confirm that this family of codes is perfect to be run on a Beowulf cluster.
Beside this observation there are two other issues we want to discuss:
We thank C. Cavazzoni to help us testing his FPMD code on our cluster and G. Settanni for allowing us to test his system simulated using Amber 5.0. S. Fantoni is aknowledged for providing us his QMC code. This work benefits from a grant for applied research from FVG region.