Project Babylon is an OpenJDK effort to let developers represent suitable Java code in a form that tools can transform for foreign programming models and runtimes. GPU programming is its most developed example in the cited JavaOne presentation, through the Heterogeneous Accelerator Toolkit (HAT). This is a project direction, not a guarantee that ordinary Java programs already run on every GPU: the presentation says GPU translation is partial and provides no stable vendor or device compatibility matrix.
What Project Babylon proposes
Java developers who target non-Java environments may need to write code in another language or build code-model scaffolding to describe their computations. Project Babylon aims to make it possible to express code for foreign programming models in Java, then use tools to validate and transform appropriate parts for execution on foreign runtimes.
In Oracle Java Platform Group presenter Paul Sandoz’s JavaOne 2026 presentation, examples included CUDA and GPU execution, ONNX machine-learning models, type-safe SQL, eBPF, and transforming Java code. The common idea is to work with a representation of Java code that tools can inspect and adapt, rather than treating Java only as a language that runs on the JVM.
Code reflection is the enabling idea
Babylon’s key mechanism is code reflection: a standard way to access Java methods and lambdas at runtime, and eventually at compile time, and represent them symbolically in a Java code model. A tool can then analyze that representation and translate suitable code into a target model. The translation is necessarily constrained by what the target can express.
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How Java could run suitable code on a GPU
The presentation uses HAT, the Heterogeneous Accelerator Toolkit, as an example of the proposed workflow: develop portable Java code, debug on the CPU, and run suitable computations on a GPU. Babylon’s code representation and translation address how Java code can be expressed for a foreign GPU model; the translated code still has to work with the relevant GPU compiler and runtime.
- Write a suitable computation in Java. Its operations and data flow must fit the subset of Java that the translation implementation can represent for the target GPU model.
- Represent and translate the code. Code reflection provides a symbolic representation that tooling can transform into foreign GPU code.
- Connect to the native toolchain. Project Panama’s Foreign Function and Memory (FFM) API can call native functions and work with native memory; HAT uses this kind of interoperation to reach foreign GPU compilers and runtimes.
- Test on the intended backend. CPU debugging can be part of the workflow, but it does not establish that a particular GPU, vendor, or backend is supported. Check the specific HAT implementation and its requirements.
Sandoz described the attraction as “The prospect of writing ordinary portable Java code that is type safe, testable, able to call methods, and able to represent GPU code is extremely attractive”. That is the intended developer experience, not a report that every Java program or GPU setup already meets it.
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Can all Java code be translated to GPU code?
No. The JavaOne presentation states: “Not all Java code is representable as GPU code, translation is partial”. Java is a general-purpose language, while a GPU execution model imposes constraints on what computations can be translated. A Babylon or HAT implementation therefore has to support particular code patterns and account for the target GPU model.
The presentation does not define a universal supported subset, name a required hardware model, or provide a stable vendor, device, or backend compatibility matrix. It also describes goals and architecture rather than benchmark results, so it does not establish a speedup or performance advantage.
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Babylon and Panama solve different parts of the problem
Project Panama focuses on connecting the JVM with native libraries and APIs, including native function calls, native data access, data layouts, and tools such as jextract. Oracle’s Java SE 26 documentation describes FFM as enabling Java programs to call native libraries and process native data outside the Java runtime without JNI.
Babylon extends the idea from calling foreign code to representing Java code and transforming appropriate portions into foreign programming models. In the HAT example, Babylon provides the code representation and translation concept, while Panama FFM provides a way to call the foreign compiler and runtime APIs.
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| Project or tool | Role in this topic | What the cited material establishes |
|---|---|---|
| Project Babylon | Represent and transform suitable Java code for foreign programming models and runtimes. | Proposed direction and code-reflection approach described in the JavaOne 2026 presentation. |
| Project Panama / FFM | Connect Java code to native functions and memory. | Project scope described by OpenJDK; FFM capabilities documented for Java SE 26. |
| HAT | GPU toolkit example combining Java code translation with native compiler/runtime access. | Presented as an example workflow; the cited material does not establish universal hardware support or a finalized Java SE feature. |
References: OpenJDK Project Panama and Oracle’s Java SE 26 Foreign Function and Memory API documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What developers should check before evaluating HAT
The presentation supports an architectural overview, not a purchasing or compatibility decision. To assess an implementation, check these points in its own current documentation:
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- Supported code patterns: which Java constructs the translator can represent, and which it cannot.
- GPU and backend support: the vendors, devices, drivers, compiler versions, and runtimes actually supported.
- Data movement: how data reaches the accelerator and returns, and what memory-management work the developer must do.
- Development workflow: what CPU debugging and testing cover, and what must be validated on the GPU.
- Maturity and portability: whether the toolkit is experimental or released, and whether code transfers between backends in practice.
- Performance evidence: any benchmarks should identify the workload, hardware, software versions, and comparison baseline; the JavaOne presentation supplies no HAT benchmark.
Oracle’s Java SE 28 early-access package documentation discusses types including MemorySegment, Arena, SymbolLookup, FunctionDescriptor, and Linker, but labels the specification draft and subject to change. Treat it as early-access material, not a statement of finalized future JDK details: Java SE 28 early-access java.lang.foreign package documentation.
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