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.
[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]
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.
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.
In my opinion, the major potential in parallel computing lies in the software part. Hardware architectures have been constantly evolving since the last 40 years and sooner or later saturation may start. The number of transistors cannot keep increasing forever. Even though software has evolved, it’s still not up to pace. There is a dearth of programmers trained to design and program parallel systems. Intel recently launched Parallel Computing Center program with the main purpose as “keeping the parallel software in sync with the parallel hardware”. The international community needs to develop the parallel programming skills to keep pace with the new processors being created. As this realization spreads, the parallel architectural landscape will touch even greater heights than expected.
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.
The performance of the single core processor has hit the wall because of power requirements and heat dissipations. Then Hardware industry started creating multicore CPUs. Although, they can compute millions of instructions per second, there are some computational problems that are so complex that a powerful microprocessor needs years to solve them.
Nowadays, there is a persistent demand for greater computational power to process large amount of data. HPC makes previously unachievable calculations possible. Today the modern computer architectures are relying more and more upon hardware level parallelism. They attain computing performance, through realization of multiple execution units, pipelined instructions [1] and multiple CPU cores [2]. The largest and fastest computers use both shared and distributed memory architectures. Contemporary trends show that the hybrid type of memory architectures will continue to prevail [3].
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.
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 event may occur in multiple resources during the same time interval and pipelined event may occur in overlapped time spans.
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
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.
motivated by the insatiable demand for more software features produced more rapidly under more competitive pressure to reduce cost.