Abstract— To improve the efficiency of computation and to reduce its execution time becomes necessary for any large application which consumes huge processing cycle. For this reason many concepts and programming models are evolved. Out of those concepts concurrent execution of small units of the same application gives better performance. This paper addresses the comparison of efficiency and performance of computing in multiprocessor and multicomputer environment. A set of tasks having various sizes is run in normal programming environment, multiprocessor environment and multicomputer environment in order to analyze the performance. The empirical results illustrate that the performance is optimal in multiprocessor (Cluster) system. Keywords - Multiprocessor System, Multicomputer System, Grid Gain, Multi Threading, Middleware, Cluster I. INTRODUCTION The complexity of working scenario is increasing now- days. To reduce the time duration and improve the efficiency of various applications many programming models evolved. Multiprocessor system and multicomputer system are widely used these days. In multiprocessor systems the system contains more than one processor which share a common memory block known as shared memory. Where in multicomputer environment multiple computer systems are there each having their own processor and memory and that memory blocks are known as distributed memory. All the systems are connected with each other in a centralized network. The multicomputer system may contain heterogeneous devices which may also effect the program execution. Applications can be deployed in any of these environments using some libraries, APIs and middleware. For deploying an application in multiprocessor system multi-threaded programming model has been used. Libraries like OpenMp, JOMP support this concept. OpenMP is developed in C language and it is compatible with C and C++. JOMP [1] is developed in Java language. However we can use Java’s inbuilt thread class and concurrency package to develop multiprocessor applications. To develop and run applications in distributed environment MPI [2], MPJ, Grid Gain [3, 4], Hadoop etc are used. MPI and MPJ are libraries which are developed in C and Java respectively. Grid Gain and Hadoop are middleware which take the task and invoke in the grid environment. Parallelism can be achieved in loop level through threads in which threads divide the loop iterations among them and executed the iterations concurrently [5, 6]. In our experiment the task is executed using loops. For this we have used multithreaded model to share the task among threads and run them concurrently. For our experiment we have used Thread Class and Concurrency package (java.util.concurrency) [7, 8, and 9] for multiprocessor application and Grid Gain Middleware for multicomputer application. In our experiment we have executed the task [10, 11] in cluster (Serial Execution), in hybrid system and distributed system grid gain as middleware and in again in cluster (Parallel Execution using Threads). All the programs run in java virtual machine ® (Version 6.5) In section II we have discussed about the concept of multiprocessor system and multicomputer system. Section III describes the multi-threading and threaded programming model. The Grid Gain (Grid Middleware) and working mechanism of Grid Gain are described in Section IV. Section V describes the set up of cluster and grid environment. It also describes the procedure to use multithreaded programming model. And finally the comparison of performance between multiprocessor (Cluster) and multicomputer (Desktop Grid) systems is described in section VI. II. MULTIPROCESSOR SYSTEM AND MULTICOMPUTER SYSTEM A Multiprocessor System contains more than one fully programmable processors [12], each capable of executing its own programs. Multiprocessors are multiple CPU Systems which access a shared memory. In this system each processor can access the main memory along with other peripherals to process programs simultaneously. The Comparative Analysis of Workflow and Performance Characteristics in Cluster and Desktop Grid K Sudipta Achary 1 , Arkaprava Bhaduri Mandal 2 , Motahar Reza 3 School of Computer Science and Engineering National Institute of Science and Technology Palur Hills, Berhampur, 761008 1 sudipta.achary@gmail.com, 2 arkabhaduri@gmail.com, 3 reza@nist.edu 978-1-4673-1344-5/12/$31.00 ©2012 IEEE