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    <p>Using regular arrays may be inefficient, as the VM may copy the array when passing it to native code, and may also use intermediate memory during I/O.</p> <p>For the fastest IO, use <a href="http://java.sun.com/j2se/1.4.2/docs/api/java/nio/ByteBuffer.html#allocateDirect(int)" rel="nofollow noreferrer">ByteBuffer.allocateDirect</a> to allocate a byte buffer. The underlying array is "special" in that it is not part of the regular JVM heap. Native code and I/O can access the array directly. </p> <p>To read data into the buffer use,</p> <pre><code>ByteBuffer byteBuffer = ByteBuffer.allocateDirect(randomAccessFile.length()); RandomAccessFile.getChannel().read(byteBuffer, 0); </code></pre> <p>To get the backing array to pass to JNI use</p> <pre><code>byte[] byteArray = byteBuffer.array(); </code></pre> <p>You can then pass this array and the file length to JNI.</p> <p>The direct buffers are realtively heavy to create, As all your files are 1MB (or thereabouts) you should be able to reuse the same buffer on multiple files.</p> <p>Hope this helps!</p>
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    1. POHow to pass a file (read from Java) most effectively to a native method?
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    1. POHow to pass a file (read from Java) most effectively to a native method?
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    1. COThanks for your answer mdma! I'm just wondering, how can I be sure that array() will work? The Javadoc says "Invoke the hasArray method before invoking this method in order to ensure that this buffer has an accessible backing array." And allocateDirect() tells me "Whether or not it has a backing array is unspecified." I wonder if that wil work?
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    2. COThat's the rub with some of the more platform-dependent features, it's VM dependent. You could catch the Excption thrown by array() and obtain the array using ByteBuffer.get(byte[]) as a fallback. If you really direct access for all VMs, you could code a small JNI stub method that takes the direct ByteBuffer instance, and calls GetDirectByteBufferAddress, which you then forward to your original JNI method. If the ByteBuffer has to copy the data once to a new array, it's going to be quick - these are optimized methods, and much quicker than reading a file piecemeal into a byte[].
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    3. COOne other point that may help your performance - use multi-threading. Even though your app is going to be I/O bound, the I/O will block waiting for data (e.g. non-contiguous files.) Having several threads reading different files simultaneously will give you a speedup, especially using async I/O. The ForkJoin framework (JSR 166) is really useful for this kind of work, and very easy to use: Refactor the file operation as a Task. Create a Task for each file you want to process, and put them all in the task queue. The task queue then runs these tasks at the level of parallelism you specify.
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