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We now show a slightly longer sample of asynchronous I/O processing. Our running sample is an application that must read a large number of image files and perform some processing on them. This kind of application may be compute bound (if the processing takes a long time and the file system is fast) or I/O bound (if the processing is quick and the file system is slow). Using asynchronous techniques tends to give good overall performance gains when an application is I/O bound and can also give performance improvements for compute-bound applications if asynchronous operations are executed in parallel on multicore machines. Listing 13-6 shows a synchronous implementation of our image transformation program. Listing 13-6. A Synchronous Image Processor open System.IO let numImages = 200 let size = 512 let numPixels = size * size let MakeImageFiles() = printfn "making %d %dx%d images... " numImages size size let pixels = Array.init numPixels (fun i -> byte i) for i = 1 to numImages do System.IO.File.WriteAllBytes(sprintf "Image%d.tmp" i, pixels) printfn "done." let processImageRepeats = 20 let TransformImage(pixels, imageNum) = printfn "TransformImage %d" imageNum; // Perform a CPU-intensive operation on the image. pixels |> Func.repeatN processImageRepeats (Array.map (fun b -> b + 1uy)) let ProcessImageSync(i) = use inStream = File.OpenRead(sprintf "Image%d.tmp" i) let pixels = Array.zero_create numPixels let nPixels = inStream.Read(pixels,0,numPixels); let pixels' = TransformImage(pixels,i) use outStream = File.OpenWrite(sprintf "Image%d.done" i) outStream.Write(pixels',0,numPixels) let ProcessImagesSync() = printfn "ProcessImagesSync..."; for i in 1 .. numImages do ProcessImageSync(i) We assume the image files are already created using the following code:

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I'm just pointing out that the PGA_AGGREGATE_TARGET is more of a request than a hard limit..

So, which method should you use, manual or automatic My preference is to use the automatic PGA memory management by default.

> SystemEnvironmentCurrentDirectory <- __SOURCE_DIRECTORY__;; val it : unit = () > MakeImageFiles();; val it : unit = () We have left the transformation on the image largely unspecified, such as the function TransformImage By changing the value of processImageRepeats, you can adjust the computation from compute bound to I/O bound The problem with this implementation is that each image is read and processed sequentially, when in practice multiple images can be read and transformed simultaneously, giving much greater throughput Listing 13-7 shows the implementation of the image processor using an asynchronous workflow Listing 13-7 The Asynchronous Image Processor open MicrosoftFSharpControl open MicrosoftFSharpControlCommonExtensions let ProcessImageAsync(i) = async { use inStream = FileOpenRead(sprintf "Image%dtmp" i) let! pixels = inStreamReadAsync(numPixels) let pixels' = TransformImage(pixels,i) use outStream = FileOpenWrite(sprintf "Image%ddone" i) do! outStreamWriteAsync(pixels') } let ProcessImagesAsync() = printfn "ProcessImagesAsync...

Caution I'll repeat this from time to time in this book: please do not make any changes to a production

system a live system without first testing for any side effects. For example, please do not read this chapter, check your system and find you are using manual memory management and then just turn on automatic memory management. Query plans may change, and performance may be impacted. One of three things could happen: Things run exactly the same. Things run better than they did before. Things run much worse than they did before.

"; let tasks = [ for i in 1 . numImages -> ProcessImageAsync(i) ] AsyncRun (AsyncParallel tasks) |> ignore printfn "ProcessImagesAsync finished!"; On the one of our machines, the asynchronous version of the code ran up to three times as fast as the synchronous version (in total elapsed time), when processImageRepeats is 20 and numImages is 200 A factor of 2 was achieved consistently for any number of processImageRepeats since this machine had two CPUs Let s take a closer look at this code The call AsyncRun (AsyncParallel ..) executes a set of asynchronous operations in the thread pool, collects their results (or their exceptions), and returns the overall array of results to the original code The core asynchronous workflow is introduced by the async { .. } construct Let s look at the inner workflow line by line: async { use inStream = FileOpenRead(sprintf "Image%d.

Just as there are two ways to manage PGA memory, there are two ways to manage SGA memory starting in Oracle 10g and above: manually by setting all of the necessary pool and cache parameters, and automatically by setting just a few memory parameters and a single SGA_TARGET parameter. By setting the SGA_TARGET parameter, you are allowing the instance to size and resize various SGA components.

In Oracle 10g and above, memory-related parameters are classified into one of two areas: Auto-tuned SGA parameters: Currently these are db_cache_size, shared_pool_size, large_pool_size, and java_pool_size. Manual SGA parameters: These include log_buffer, streams_pool, db_Nk_cache_size, db_keep_cache_size, and db_recycle_cache_size.