Primitive ARK Total Conversion Activation Code
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In the above example, for each Order in the Working Memory, the engine will execute the init code initializing the total variable to zero. Then it will iterate over all OrderItem objects for that order, executing the action for each one (in the example, it will sum the value of all items into the total variable). After iterating over all OrderItem objects, it will return the value corresponding to the result expression (in the above example, the value of variable total). Finally, the engine will try to match the result with the Number pattern, and if the double value is greater than 100, the rule will fire.
The conditional element eval is essentially a catch-all which allows any semantic code (that returns a primitive boolean) to be executed. This code can refer to variables that were bound in the LHS of the rule, and functions in the rule package. Overuse of eval reduces the declarativeness of your rules and can result in a poorly performing engine. While eval can be used anywhere in the patterns, the best practice is to add it as the last conditional element in the LHS of a rule.
Drools attempts to preserve numbers in their primitive or object wrapper form, so a variable bound to an int primitive when used in a code block or expression will no longer need manual unboxing; unlike Drools 3.0 where all primitives were autoboxed, requiring manual unboxing. A variable bound to an object wrapper will remain as an object; the existing JDK 1.5 and JDK 5 rules to handle auto-boxing and unboxing apply in this case. When evaluating field constraints, the system attempts to coerce one of the values into a comparable format; so a primitive is comparable to an object wrapper.
The runtime automatically handles object layout and manages references to objects, releasing them when they're no longer being used. Objects whose lifetimes are managed in this way are called managed data. Garbage collection eliminates memory leaks and some other common programming errors. If your code is managed, you can use managed, unmanaged, or both managed and unmanaged data in your .NET application. Because language compilers supply their own types, such as primitive types, you might not always know or need to know whether your data is being managed.
Successful specification of the two mouse blastocyst inner cell mass (ICM) lineages (the primitive endoderm (PrE) and epiblast) is a prerequisite for continued development and requires active fibroblast growth factor 4 (FGF4) signaling. Previously, we identified a role for p38 mitogen-activated protein kinases (p38-MAPKs) during PrE differentiation, but the underlying mechanisms have remained unresolved. Here, we report an early blastocyst window of p38-MAPK activity that is required to regulate ribosome-related gene expression, rRNA precursor processing, polysome formation and protein translation. We show that p38-MAPK inhibition-induced PrE phenotypes can be partially rescued by activating the translational regulator mTOR. However, similar PrE phenotypes associated with extracellular signal-regulated kinase (ERK) pathway inhibition targeting active FGF4 signaling are not affected by mTOR activation. These data indicate a specific role for p38-MAPKs in providing a permissive translational environment during mouse blastocyst PrE differentiation that is distinct from classically reported FGF4-based mechanisms.
The basic idea is simple. We divide the large array into blocks that each can be scanned by a single thread block, and then we scan the blocks and write the total sum of each block to another array of block sums. We then scan the block sums, generating an array of block increments that that are added to all elements in their respective blocks. In more detail, let N be the number of elements in the input array, and B be the number of elements processed in a block. We allocate N/B thread blocks of B/2 threads each. (Here we assume that N is a multiple of B, and we extend to arbitrary dimensions in the next paragraph.) A typical choice for B on NVIDIA 8 Series GPUs is 128. We use the scan algorithm of the previous sections to scan each block i independently, storing the resulting scans to sequential locations of the output array. We make one minor modification to the scan algorithm. Before zeroing the last element of block i (the block of code labeled B in Listing 39-2), we store the value (the total sum of block i) to an auxiliary array SUMS. We then scan SUMS in the same manner, writing the result to an array INCR. We then add INCR[i] to all elements of block i using a simple uniform add kernel invoked on N/B thread blocks of B/2 threads each. This is demonstrated in Figure 39-6. For details of the implementation, please see the source code available at -gpugems3/.
After optimizing shared memory accesses, the main bottlenecks left in the scan code are global memory latency and instruction overhead due to looping and address computation instructions. To better cover the global memory access latency and improve overall efficiency, we need to do more computation per thread. We employ a technique suggested by David Lichterman, which processes eight elements per thread instead of two by loading two float4 elements per thread rather than two float elements (Lichterman 2007). Each thread performs a sequential scan of each float4, stores the first three elements of each scan in registers, and inserts the total sum into the shared memory array. With the partial sums from all threads in shared memory, we perform an identical tree-based scan to the one given in Listing 39-2. Each thread then constructs two float4 values by adding the corresponding scanned element from shared memory to each of the partial sums stored in registers. Finally, the float4 values are written to global memory. This approach, which is more than twice as fast as the code given previously, is a consequence of Brent's Theorem and is a common technique for improving the efficiency of parallel algorithms (Quinn 1994).
Rather than write a custom scan algorithm to process RGB images, we decided to use our existing code along with a few additional simple kernels. Computing the SAT of an RGB8 input image requires four steps. First we de-interleave the RGB8 image into three separate floating-point arrays (one for each color channel). Next we scan all rows of each array in parallel. Then the arrays must be transposed and all rows scanned again (to scan the columns). This is a total of six scans of width x height elements each. Finally, the three individual summed-area tables are interleaved into the RGB channels of a 32-bit floating-point RGBA image. Note that we don't need to transpose the image again, because we can simply transpose the coordinates we use to look up into it. Table 39-1 shows the time spent on each of these computations for two image sizes. 153554b96e
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