I'm developing a plain-Java library for decoding, encoding, demuxing, muxing, and processing images, audio, and video using the Vector API. Its optimized performance is promising, and I'm aiming to compete with native tools such as ffmpeg, but startup warmup is a serious issue. Before C2 compiles Vector API calls into SIMD instructions, they run interpreted and can be much slower than the scalar fallback, causing the first audio or video frames to lag.
I investigated Project Leyden's AOT cache. A library cannot currently distribute its own complete cache because the cache depends on the user's hardware and runtime, although a Maven or Gradle plugin might be able to collect training workloads from dependencies and execute them during the application build. The code-cache functionality I need is also targeted for a future JDK release, so I experimented with an early-access/custom branch.
I also had to work around restrictions involving the incubating Vector module and discovered that the cache still did not optimize my code correctly. C2 needs the vector species—the vector class and lane count—to be compile-time constants. My library stores species in static final fields, such as a 128-bit short-vector species. During training, C2 can fold those values after class initialization, but the stored code cannot safely assume that another machine will initialize the fields to identical values.
I modified my JDK fork so the trained species values are restored with the cached code and checked cheaply at runtime. If the production CPU does not match the trained vector class and lane count, the cached code is discarded and normal JIT compilation takes over. This removed the warmup penalty in my tests.
Is there a better way to address this? Could future Vector API, Valhalla, or Leyden improvements make interpreted Vector code close enough to scalar performance during warmup, or is an AOT/code cache approach the more realistic solution?
4 Answers
OpenJ9 is worth investigating as a comparison point. It has supported shared class data and AOT/JIT caches for a long time, including ways to reuse or distribute cache data with an application. The tradeoff is that users would need to run OpenJ9, and its Vector API behavior may not match HotSpot, so it may not solve the portability problem directly.
For immediate startup issues, an explicit warmup phase could be simpler than modifying the VM. Run representative codec operations during application startup, measure until the optimized path reaches an acceptable throughput, and stop after a timeout or a fixed budget. That adds startup work, but it can prevent the first real frames from paying the compilation cost.
The distinction between an application cache and a library cache is important. Leyden can support distributing a cache with an application, but a library generally cannot ship a universally valid partial cache because the final runtime, CPU, options, and dependency graph are controlled by the application. A build plugin that discovers training artifacts from dependencies and runs them together before packaging sounds like a practical way to improve the experience.
Publishing training workloads as a test-jar-style artifact could fit well into the Java build ecosystem. Each library could expose optional representative workloads, and the application build would execute all selected workloads in one training JVM before creating its final cache. The build would still need to invalidate or regenerate the cache when the CPU, JVM flags, Vector species, or dependency versions change.
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