Computational Scale Out Workloads Applications

Description and relevant performance metrics: Digital Computers with 2688 Intel Itanium Processors and 384 MIPS Processors distributed amongst 10 single image NUMA-based clusters. Individual clusters have a compute capability in excess of 190 million MTOPS

Since the invention of the first computer, engineers have been conceptualizing and implementing ways to optimize system performance. The last 25 years have seen a rapid evolution of many of these concepts, particularly cache memory, virtual memory, pipelining, and reduced set instruction computing (RISC). Individual each one of these concepts has helped to increase speed and efficiency thus enhancing overall system performance. Most systems today make use of many, if not all of these concepts. Arguments can be made to support the importance of any one of these concepts over one

As technology advances, the processes that we use to manage that technology become more demanding, creating the need for new software and efficient processors. “The central processing unit or (CPU) is the heart of your computer and is used to run the operating system as well as all the programs.” (Chris Hoffman, CPU Basics: multiple CPU’s, cores and hyper threading explained.) With so much power in a single chip, we have created a powerful piece of technology that can be placed virtually anywhere.

More cache means better performance, so modern processors are always looking for new and innovative ways to add more cache to a single die. Furthermore, cache is such an important feature that due to the expense of making it, most caches are divided into 3 levels of varying speeds and sizes. The highest capacity, but slowest in performance is the L3 cache, originally on the motherboard itself, it is now integrated into the processor and generally shared between all cores (Labsim). The next level is the L2 cache, faster than L3, but smaller in size. The L2 cache is located on the processor and usually has multiple of this cache for either private, per core cache, or only shared between a select few cores. The final and fastest of the caches is the L1 cache, but smallest in size of all the others. L1 is located on the processor and private to every core for quick access of small amounts of vital information

The multikernel is a distributed OS architecture for heterogeneous multicore machines that communicate with message passing only.

Generally the CPU has fetch, decode, and execute operation. Most of today’s multi-core processors feature shared caches. In shared caches the caches are splitted into blocks and are used randomly by each core in a multi-core processor. Because of this there is severe delay experienced by each core and the processing speed is also reduced. This happens due to more time taken for read operation of memory and CPU. The problem faced by such architectures is cache contention. So far the time required for fetching is more when compared to the execution process. The fetching time must be reduced. Hence to address this problem, we have implemented a program that allows the usage of cache memory for each core of a processor at same time. In our concept, we execute all jobs of each

Main the main changes in hyperscale computing include: local network-connected storages substitute the traditional array of storage networks, virtual LANs takes over dedicated computing, commodity network elements replace network switching, commodity computing components substitute blade systems, and new applications and software replace the existing hardware devices meant for tracking and monitoring.

Integrating the main memory onto the CPU can drastically improve the computation speed by eliminating the I/O bottleneck to off-chip

Stratoscale is focused on leveraging technology to help IT teams, within the service provider, make better and more profitable usage of existing infrastructures. Service Provider data center requirements are growing at an ever-increasing pace. In response to this changing and challenging landscape, Stratoscale has built a hardware-agnostic hyper-converged software solution that facilitates scale-out, simplifies operations and allows your IT infrastructure to keep up with your, and your customers’, business growth.

Both computing types involve multitenancy and multitask. This means many customers can perform different tasks by accessing a single or multiple instances of resources. Sharing resources help in reducing peak load capacity [32].

Run SAP HANA workloads like SAP Business Warehouse (BW) or other OLAP scenarios on a multi-node cluster, providing up to 4 TB of memory.

This paper gives an overview about Grid Computing and the various key issues related to that.Today we are in the Internet world and everyone prefers to get fast access to the Internet. The growth of the internet with various resources and high speed networks changes the society enormously. We can improve the speed of the computer using Grid. This paper presents clearly the vision of the grid computing and the architecture of grid computing.The advantages and the disadvantages of the grid computing has also been discussed.

Since the earliest digital electronic computers, instruction sets have tended to grow larger and more complex. The MARK-1 (in 1948) had seven instruction of trivial complexity, yet a contemporary machine like the VAX has more than three hundred instructions. In addition, its instructions can be more complicated. Any high performance implementation of the VAX has to rely on complex implementation techniques such as pipelining, caches and prefetching.

For main memory performance benchmarking, Stream benchmark algorithm is used. This algorithm measures the sustainable real world bandwidth as real users faced, not the theoretical peak values. It is a widely known and applied algorithm in memory bandwidth performance tests [27]. The Stream algorithm is also a synthetic benchmark which creates synthetic workloads, not the real world workloads. The benchmark measures the bandwidth performance by four long vector / matrix operations, like “copy”, “scale”, “add”, and “triad” (combination of other three). The algorithm assigns one of the arrays to another in order to measure the “copy” operation time and bandwidth. For “scale” operation, it multiplies the elements of an array by a scalar value. In “add” operation, it adds the elements of an array to the elements of another array and assigns the results to a third array. “Triad” does the calculations of the three operations all together (copy, scale, and add). In our evaluations, we have used the average of the results for these four

4. Performance Comparison of Dual Core Processors Using Multiprogrammed and Multithreaded Benchmarks ............................................................................................... 31 4.1 Overview ........................................................................................................... 31 4.2 Methodology ..................................................................................................... 31 Multiprogrammed Workload Measurements .................................................... 33 4.3 4.4 Multithreaded Program Behavior ..................................................................... 36 5. 6. Related Work ............................................................................................................ 39 Conclusion ................................................................................................................ 41