We propose a new approach that automatically parallelizes Java programs. The approach collects
on-line trace information during program execution, and dynamically recompiles methods that can
be executed in parallel. We also describe a cost/benefit model that makes parallelization decisions, as
well as a parallel execution environment to execute parallelized code. We implement these techniques
upon Jikes RVM. And finally, we evaluate our approach by parallelizing sequential benchmarks and
comparing the performance to manually parallelized version of those benchmarks. According to the
experimental results, our approach brings low overheads and achieves competitive speedups compared
to manually parallelized code.
1 Introduction
Multi-processor has already become mainstream in both personal and server computers. Even on em-
bedded devices, CPUs with 2 or more processors are increasingly used. However, software development
does not catch up with hardware at this time. Designing programs for multi-processor computers is still
a difficult task and requires a lot of experiences and skills. Besides, a big number of legacy programs
that are designed for single-processor computers are still running and need to be parallelized for better
performance. All these facts require a good approach for program parallelization.
Compiler that automatically parallelize source code into executable code for multi-processor comput-
ers is a very promising solution to this challenge. A lot of research works have been done in this field,
which shows the capability of compiler based parallelization, especially for scientific and numeric appli-
cations. However, There are several limitations in these approaches due to the lack of dynam...
... middle of paper ...
...es. To deal with this problem, we compact one section of contin-
uous addresses into one data record, which is proved to be able to save a lot of memory space in our
experiments.
We also try to simplify dependency analysis by introducing dependent section, which is a section
in a trace containing all instructions dependent to another trace. For instance, a single loop has 100
instructions and the 80th and 90th instructions carry dependency between loop iterations. In this case
the dependent section of loop body trace is 80 to 90 after dependency analysis. Dependent section is used
based on the observation that mostly only a small section of instructions in a trace carries dependency.
Besides, using single dependent section for each trace greatly reduces synchronization/lock overheads in
the busy-waiting mode that is used in our parallel execution model.
The inter-temporal relationship between every task was specified in advance so the impact of delay of a task on other tasks could be calculated.
[1] End-To-End Arguments In System Design by J.H. Saltzer, D.P. Reed and D.D. Clark [M.I.T. Laboratory for Computer Science]
Delivering computer solutions has changed radically over the past thirty years from centralised mainframe computing to distributed client-server solutions. The consumption of Information Technology and Services (IT&S) has been accelerated by advances in network performance and facilities, consumerisation, and most notably through the adoption of Internet services. Business applications have also gone through a similar change from bespoke in-house mainframe systems to packaged products, and more recently, to distributed application frameworks (as seen on the iPhone).
A program can be broken down into several smaller units, which has a particular task or has a repeated task. The complete program is thus made up of multiple smaller, independent subprograms that work together with the main program.
To build more powerful microprocessors requires an expensive and intense production process. Some computations take years to solve even with the more powerful microprocessor. May be because of these factors, programmers sometimes use a different approach called parallel processing.
J. R. Graham, “Comparing parallel programming models,” Journal of Computing Sciences in Colleges, 23(6):65-71, 2008.
32 bit chips which are constrained to a maximum of 2 GB user addressable or 4 GB of RAM access, sped up this transition. A 64-bit chip addressing space is increased to 2^64 bytes of RAM and can greatly increase system performance and the way programs can be written without having to take in consideration the above constraints.
MC68060 uses branch cache to let the instruction fetch pipeline notice about the branch instructions such as jmp or procedures, and change the instruction stream timely.
The Von Neumann bottleneck is a limitation on material or data caused by the standard personal computer architecture. Earlier computers were fed programs and data for processing while they were running. Von Neumann created the idea behind the stored program computer, our current standard model. In the Von Neumann architecture, programs and data are detained or held in memory, the processor and memory are separate consequently data moves between the two. In that configuration, latency or dormancy is unavoidable. In recent years, processor speeds have increased considerably. Memory enhancements, in contrast, have mostly been in size or volume. This enhancement gives it the ability to store more data in less space; instead of focusing on transfer rates. As the speeds have increased, the processors now have spent an increasing amount of time idle, waiting for data to be fetched from the memory. All in all, No matter how fast or powerful a...
Concurrent engineering is a method for breaking down the product advancement of a vast provision into more diminutive lumps. In iterative or concurrent engineering, characteristic code is outlined, created and tried in rehashed cycles. With each one emphasis, extra characteristics could be outlined, created and tried until there is a completely useful programming requisition primed to be sent to clients.
There must be enough memory DIMMs populated per processer to equalize with the number of memory channels.
Parallel computer are those system that emphasis parallel processing. The basic architectural features of parallel computers are introduced below. We divide parallel computer into three architectural configuration :
The author mentioned his system, what the objective from this system. But he didn't declare what are the techniques used and how these techniques worked to perform this work, there are no details, and the work method was ambiguous to me, so it is difficult to benefit from this paper.
It can be identified as the quantity of data transferring between nodes toward the end of execution stage as this is the data that will be processed further in the execution stage. In the DSM system the quantity of data sharing between nodes is normally based on different physical page-size. The system utilizing paging, in spite of the measure of data sharing, the measure of data transferring between nodes is normally based on different physical page size of the fundamental architecture. Issue emerges when system that comprises very small data granularity are running on system that backing very large physical pages. On the off chance that the shared data is saved in adjacent memory area then most data can be saved in couple of physical pages. Subsequently lower the efficiency of system as the common physical page hits between multiple processors. To resolve this issue the DSM system subdivided the shared data structure on to disjoint physical
motivated by the insatiable demand for more software features produced more rapidly under more competitive pressure to reduce cost.