French Firm Brews Parallel Java Offering

By Michael Feldman

July 8, 2010

Yet another software toolmaker has offered up its solution to the parallel programming crisis. This week, French software vendor Ateji released a Java solution for multicore CPUs and multiprocessor server environments. Ateji PX for Java is aimed at developers who want to take advantage of current and future computer architectures by moving their legacy codes into the parallel realm.

Now Java is not exactly the first language that comes to mind when you think of parallel programming. Although the language encompasses the thread model and the notion of concurrency, it’s up to the Java Virtual Machine (JVM) component to implement true parallelism on the hardware. Attempts to add more parallel smarts to the language itself are numerous, but mostly incomplete. These include Titanium (a parallel dialect of Java), JOMP (an OpenMP-supported implementation), and JavaParty (for distributed computing environments), among others. Despite all these research projects, commercial adoption of parallel Java implementations never took hold.

Ateji, of course, would like to change that. One thing in its favor is that Ateji PX supports a wide range of parallel models, including task parallelism, data parallelism, speculative parallelism, recursive parallelism and distributed parallelism. And it does so with a relatively-small number of extensions to the standard language. The main addition is the “||” operator, which is used to tell the compiler to parallelize the associated source statement. For example, if you wanted to parallelize matrix multiplication via a for-loop you embed the || after the keyword:

for || (int i : I) {
   for (int j : J) {
      for (int k : K) {
         C[i][j] += A[i][k] * B[k][j];
      }
   }
}

Using this method, Ateji demonstrated a 3.4x speedup on a 4-core PC and a 12.5x speedup on a 16-core server. You could implement the equivalent parallelism by using Java’s thread library, but that would take about four times as much user code. Also, in that case, the original matrix multiplication algorithm gets hidden within a lot of thread management code.

In fact, the Ateji solution tosses the whole notion of threads out the window. “Threads are very ill-suited as a programming concept,” says the Ateji PX white paper. According to a Berkeley technical report cited in the whitepaper, a thread is really a hardware concept associated with the underlying processor architecture, rather than a natural language construct. This makes the thread model a much less productive way to think about parallelizing algorithms and can lead to buggy programs.

Instead, the Ateji model introduces the concept of a “branch,” which is basically equivalent to a parallelized statement. Beneath the covers, branches do get mapped to processor threads since the hardware demands it. But the mapping is context-specific, and the programmer can’t assume that the branch is (or is not) running in its own thread.

Ateji PX does provide compatibility with standard Java. The compiler front-end does a source-to-source translation to the base language, allowing the developer to keep his or her software development tool chain intact. It also enables the developer to revert back to vanilla Java once the parallelism has been implemented.

Even though the Ateji solution is being promoted as “parallel programming made simple,” the programmer is still stuck with the task of figuring out which statements and code sequences can be parallelized. The compiler doesn’t protect you from things like data races and deadlocks, so locks and atomic operations must be used if data is to be shared across parallelized code. For cases where sharing memory becomes too arduous or is just not available, Ateji PX also offers distributed memory parallelism. In fact, the Ateji PX programming manual suggests you mix the two, using a lot of small, well-defined parallel tasks that communicate via message passing.

For distributed parallelism, Ateji has come up with message passing primitives. At the simplest level, the programmer just needs to declare a channel object for message passing that is visible to both the sender and the receiver (using || chan ! value to send a message and || chan ? value to receive it). The fact that message passing can be specified at the language level rather than by invoking runtime routines (i.e., an MPI library) means that the source can be mapped to different types of distributed computing architecture independent of library implementations. It also allows different kinds of distributed models to be used — dataflow, stream programming, the Actor model, and the MapReduce algorithm.

Apparently, the distributed parallel feature is still a work in progress. According to the Ateji Web site, a version that is able to implement parallel branches on distributed memory hardware — compute clusters, supercomputers, grids and clouds — is still under development.

Of course, the whole idea of parallelizing code is to boost execution performance. Mapping code to more than one core or processor is bound to speed up the program, but Java, being an interpreted language, is not known for its stellar performance. For optimal execution, some heavy lifting will have to be done by the JVM. Just in time (JIT) compilers have helped to some extent, but the virtual machine model tends to be at odds with compute-bound codes. A 2004 report looked at the current state of Java for HPC and found that it is possible to deliver comparable performance to that of a Fortran/MPI implementation, for at least some codes, but scalability issues still remain. It’s not inconceivable that back-end Java technology could be developed for the performance market.

Although the official launch of Ateji PX was this week, the solution has been previewed by selected customers over the past year. A group at ISIMA claimed a seven-fold speedup with the Game of Life algorithm using Ateji PX on an 8-core Nehalem system. Another researcher at the University of Pisa benchmarked different parallel implementations of the convolution algorithm using the Ateji solution. In another case, an investment bank was able to parallelize a back-office Java app, reducing execution time from 40 minutes to eight minutes.

A free 30-day evaluation license is available for users that want to give Ateji PX a whirl. The solution is provided as an Eclipse plug-in, and all documentation and samples are provided online through the Ateji Web site.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This