#Particle electron api request tutorial full#
#Particle electron api request tutorial code#

#Particle electron api request tutorial code#

Totally dependent on the programmer, who has to develop optimized code in order to

Used to solve it and the application architecture. Here, the extent that the applicationĬan benefit from additional cores depends on the computational problem, the algorithm Is not the case for additional CPUs or cores. While an increased clock cycle automatically speeds up an existing application, this These methods are what manufacturers focus on today, resulting in feature-richĬPUs that are additionally equipped with an increasing number of computational cores. Remaining basis for performance improvement apart from the gate count (Schaller, 1997 ). Schemes, speculative execution and branch prediction, which nowadays are the only These are, for example, sophisticated ILP ThereĪre other methods that are used to increase performance that can at least partly compensateįor the limited increase in clock speed. The diffraction limits of the lithographic methods used for chip manufacturing. However, reducing the physical dimensions of CPUs is limited by Is that doubling the clock speed also means halving the distance travelled by theĮlectrical signal per clock cycle, which requires the physical size of the CPU toīe twice as small. Laws of physics put an end to the increase in clock speed. `free lunch' (Sutter, 2005 Sutter & Larus, 2005 ) of performance gain is over and in recent years CPU manufacturers have started sellingĬPUs with more computational cores instead of faster CPUs, as beginning in 2003 the Up two registers and copying the result to another register). More than just one single operation within the same clock cycle (for example, summing Instruction-level parallelism (ILP), basically meaning the possibility of performing On), the number of instructions executed per time unit (clock speed) and the so-called This was the result of improvements in the gates-per-dieĬount or transistors per area (the main attribute of CPUs that Moore based his law

Moore's Law (Moore, 1965 ), which states that processing power doubles every 18 months, turned out to be correctįor the entire decade of the 1990s. In these cases for many years it has been a validĪpproach to simply wait for the hardware to become faster. Have to worry about performance as in recent decades CPU manufacturers have improved How can computational speed be improved?įor many standard applications, for a very long period of time programmers did not Power is thus essential to keep up with modern scientific technologies.ġ.2. Power can be increased by at least two orders of magnitude. In some areas future applications will only be possible if the computational Significantly more computational power than the current improvement in CPU technologyĬan offer. Certain applications, suchĪs the alignment of large numbers of single-particle cryo-EM images, will require Increase is already based on multi-core CPU architecture. (CPU) technology that can be expected in the future. Is certainly some linear computational speed increase in central processing unit Of structural biology are especially high for high-resolution structure determination by single-particle electron cryomicroscopy because an ever-larger number of imagesĪre currently being used to overcome the resolution limits of this technique. Resources for data processing and data storage. Molecular-biology techniques such as `deep sequencing' also require large computational Of the most compute-intense areas are in the field of structural biology, but new Plays a central role in various disciplines in biology and physics. Owing to the ever-increasing speed of data collection in science, computational performance Generation of computer hardware is outlined. Infrastructure and solution strategy for moving scientific applications to the next The tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art In this article, state-of-the-art paradigms are introduced,

#Particle electron api request tutorial full#

The full power of today's technology, makes certain applications feasible that were The increased computational performance obtained by combining all paradigms, unleashing Each parallel-processing paradigm alone can improve overall performance Games, are now ubiquitous and provide unprecedented computational power for scientificĪpplications.

(GPUs), which were originally developed for rendering real-time effects in computer Recentĭevelopments in information technology such as multicore, parallel and GPU processingĬan be used to overcome these limitations. Most known methods for the determination of the structure of macromolecular complexesĪre limited or at least restricted at some point by their computational demands.