How many Integer Operations on a GPU are necessary for one Hash?

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One compute-intensive part of the server work is creating new merkle roots. As mckoss mentions this becomes a lot faster if you only recalculate the merkle branch that is changed by the new generation transaction instead of recalculating the entire merkle tree.

The other compute-intensive part of the server's job is verifying the proofs of work that are later sent in. In general this means hashing two SHA-256 chunks and verifying the result against the difficulty. But if there are multiple proofs of work with the same merkle root then you could calculate the midstate once, and then just hash one SHA-256 chunk for each proof of work you want to verify. Just like clients do with rollntime.

So every new merkle root is work to generate, and also creates more work later by needing a new midstate to be calculated.

BitMinter makes use of rolling the ntime field on the server as well as in the client. Every time the wall clock ticks forward one second, you update the...

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Q: What is CUDA?

CUDA® is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

Since its introduction in 2006, CUDA has been widely deployed through thousands of applications and published research papers, and supported by an installed base of hundreds of millions of CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers. Applications used in astronomy, biology, chemistry, physics, data mining, manufacturing, finance, and other computationally intense fields are increasing using CUDA to deliver the benefits of GPU acceleration.

Q: What is NVIDIA Tesla™?

With the world’s first teraflop many-core processor, NVIDIA® Tesla™ computing solutions enable the necessary transition to energy efficient parallel computing power. With thousands of CUDA cores per...

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In games and graphics one often needs to generate pseudorandom numbers. Needless to say, PRNGs are an extremely well-researched topic; however, the majority of the literature focuses on applications with very exacting quality requirements: cryptography, high-dimensional Monte Carlo simulations, and suchlike. These PRNGs tend to have hundreds of bytes of state and take hundreds of instructions to update. That’s way overkill for many more modest purposes—if you just want to do a little random sampling in a game context, you can probably get away with much less.

To drive home just how much lower my random number standards will be for this article, I’m not going to run a single statistical test on the numbers I generate—I’m just going to look at them! The human visual system is pretty good at picking out patterns in what we see, so if we generate a bitmap with one random bit per pixel, black or white, it’s easy to see if we’re generating “pretty random” numbers—or if...

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wrote: What are you doing running that much expensive hardware on your static-prone carpet??

wrote: Putting it on the carpet was just to take pictures in an uncluttered space :-) But it does look strange... I am going to try to crop some pictures.

wrote: Great work as always! Your level of detail is appreciated.

wrote: where can i download your rainbow tables
or did you provide md5 cracking as cloud service ?
no serious, good work !!

wrote: Great post, this is awesome!

wrote: Hello,
This is great stuff.
I run a 5850@725MHz, and I see 3696 Mhash/sec. This almost seems more than expected compared to the 5970.
I was doing some opencl experiments myself, but these results will prompt me to look into CAL.
Question: The 69xx series has a VLIW4 instruction set compared to

wrote: Seems my previous post got truncated somehow.
Question is:
The 69xx series has a VLIW4 instruction set compared to...

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OFF, an open source (free software) code for performing fluid dynamics simulations, is presented. The aim of OFF is to solve, numerically, the unsteady (and steady) compressible Navier–Stokes equations of fluid dynamics by means of finite volume techniques: the research background is mainly focused on high-order (WENO) schemes for multi-fluids, multi-phase flows over complex geometries. To this purpose a highly modular, object-oriented application program interface (API) has been developed. In particular, the concepts of data encapsulation and inheritance available within Fortran language (from standard 2003) have been stressed in order to represent each fluid dynamics “entity” (e.g. the conservative variables of a finite volume, its geometry, etc…) by a single object so that a large variety of computational libraries can be easily (and efficiently) developed upon these objects. The main features of OFF can be summarized as follows:

Programming LanguageOFF is written in...

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Pushing the limits of personal computing... How much further can we go?

By Alexander J. Yee & Shigeru Kondo

(Last updated: February 23, 2013)

For anyone who is interested, this was recently the topic of a

Stack Overflow question that I answered

.

October 17, 2011: The record has been improved to 10 trillion digits.

Click here for access to some of the digits.

Computation Statistics - All times are Japan Standard Time (JST).

Here are the full computation statistics. As with all significant computations that are done using y-cruncher - A Multi-Threaded Pi Program, a screenshot and validation is included. Since the computation was done in multiple sessions, there is no single screenshot that captures the entire computation from start to finish. The screenshot provided here is simply the one that shows the greater portion of the computation.

Validation File: Validation - Pi - 5,000,000,000,000.txt


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