Comparing Performance: Python, C++, Java, and Julia

Performance – how fast a program runs – matters a great deal in computing. In tasks like heavy numerical simulation, processing large files, or running many concurrent operations, a few extra seconds (or minutes) can make a big difference. At one end of the spectrum are low-level compiled languages like C++ that give programmers direct control of the hardware, and at the other end are high-level languages like Python that prioritize ease of use but may incur overhead. Java sits in the middle as a managed language with a sophisticated runtime, while Julia is a newer language explicitly designed for scientific and numerical computing with high performance in mind. In this article, we compare how Python, C++, Java, and Julia typically perform on three kinds of tasks – numerical computation, file I/O, and concurrency – and explain what language features and architectures cause the differences.

Illustration: The speed of code

Numerical Computation

• Python: Pure Python code (e.g. plain loops with arithmetic) is generally much slower than compiled languages. In simple loop benchmarks, Python can be tens to hundreds of times slower than C or C++. This is because the Python interpreter must execute each operation at runtime, and dynamic typing adds overhead. That said, Python often uses optimized native libraries (like NumPy) for heavy numeric work; when using those, Python can leverage C speeds under the hood. Still, any computation done in “pure Python” (outside of a library) will be much slower. The Global Interpreter Lock (GIL) and lack of static types also hinder raw speed.
• C++: C++ is usually the fastest of the four for raw number-crunching. It compiles directly to machine code with optimizations tailored to the hardware, avoids runtime checks, and allows manual control of memory and CPU instructions. In general, “C++ often wins in raw execution speed” for CPU-bound loops and computations. Its strengths include avoiding things like garbage collection pauses or bounds-checking that slower languages incur. However, C++ requires careful coding; even small mistakes (like forgetting to reserve vector space) can hurt performance.
• Java: Java is slower than C++ in raw numeric speed, but often much faster than Python. Java code is compiled to bytecode which the JVM JIT compiler optimizes at runtime. Long-running numeric loops can become quite fast after “warm-up,” but Java still pays costs for features like automatic garbage collection, object overhead, and array bounds-checking. These safety and abstraction features add overhead compared to C++. In summary, Java performance usually falls between C++ and Python: it may approach C++ speeds for steady loops, but in tight numeric loops it typically lags behind due to the extra layers of the JVM and managed memory.
• Julia: Julia was designed specifically for high-performance numerical and scientific computing. It is JIT-compiled (using LLVM) so it turns Julia code into optimized machine code at runtime. Julia supports just-in-time specialization based on types, multiple dispatch, and efficient array operations, which lets it often get near C++-level speed on heavy numeric tasks. In practice, Julia often outperforms plain Python by a large factor and can rival C++ and Java for large computations. One caveat is that Julia has some compilation latency (it spends time compiling functions before running), so short tasks may feel slower. But for long numeric loops, Julia typically achieves speed very close to a compiled language, especially when code is written in a type-stable, vectorized manner. (Benchmark comparisons often find Julia “roughly [within] a few percent” of optimized C++ performance on many problems.)

In summary, for numerical work C++ is typically fastest, Julia nearly matches C++ on well-written code, Java comes next, and Python is usually slowest if pure-Python loops are used. Python can close the gap only by offloading work to optimized libraries (e.g. NumPy, which uses C), whereas C++ and Julia deliver high speed more directly from their language design.

File I/O

• Python: Python’s file I/O is easy and high-level, but each read/write call still goes through the Python runtime, which adds overhead. In a loop reading many files, Python code can be significantly slower than C. For example, one test concatenating many small files showed C about 1.5–2× faster than Python. Python uses buffered I/O under the hood and releases the GIL during blocking I/O calls, so it can handle concurrent I/O reasonably well – but purely in terms of throughput, Python usually lags behind the lower-level languages. Python’s strength is convenience (e.g. simple open() and file iterators) rather than raw speed of disk operations.
• C++: C++ typically has the fastest file I/O among these languages. Using C or C++ file streams (or C’s fread/fwrite), a program interacts closely with the operating system’s I/O. There’s minimal language runtime overhead or indirection. C++ implementations often let you tune buffering and use memory-mapped files or other techniques for maximum throughput. In practice, a well-written C++ I/O loop can saturate disk bandwidth. The trade-off is that C++ code must manage details (opening/closing files, error checking) explicitly, but this also means there’s no extra performance cost for safety checks like bounds checking on the buffers (which Java would do) or dynamic typing (which Python has).
• Java: Java’s I/O performance is generally good, especially when using java.io or java.nio classes with buffering. Java I/O APIs do introduce some object overhead (e.g. wrapping byte streams) and the garbage-collected JVM adds a layer between the code and OS. Still, Java’s I/O can approach C++ speeds if used properly. Many benchmarks find that Java’s buffered I/O is only moderately slower than C++; in some cases even faster if the JVM optimizes the code. For example, Java NIO (non-blocking I/O) can outperform plain Java IO and is designed for high-throughput applications. Overall, Java provides strong built-in file I/O support; its performance is generally competitive, though the safety of automatic memory management and checks means it won’t beat a tuned C++ loop on raw speed.
• Julia: Julia’s file I/O uses its high-level I/O functions (e.g. open, read, write), which internally call into efficient C libraries. Performance-wise, Julia I/O is similar to Python’s: comfortable to use but with some overhead. Julia does not have a GIL, so I/O functions can run concurrently, but the single-threaded default loop still does work one file at a time unless you explicitly parallelize. In absolute terms, Julia I/O is generally slower than C++ and on par with or slightly faster than Python in many cases. It has improved in recent versions, but because Julia code has its own runtime step before calling OS I/O, it usually cannot beat native C++ I/O. The advantage of Julia here is expressiveness (e.g. reading CSV files with a few lines of code) rather than raw I/O speed.

In summary, for file-based tasks C++ tends to be fastest (since it works closest to the OS), Java is also very fast with good libraries, Python is slower (often by a factor of 1.5–2 or more) due to interpreter overhead, and Julia is roughly comparable to Python in practice, trading simplicity for a bit of runtime cost. The architectural reason is that C++ and Java use compiled I/O libraries and buffering with minimal per-call overhead, whereas Python/Julia involve more runtime language layers.

Concurrency

• Python: Python’s default runtime (CPython) has a well-known limitation: the Global Interpreter Lock (GIL). The GIL is a mutex that allows only one thread to execute Python bytecode at a time. In effect, CPU-bound threads in Python cannot run in true parallel on multiple cores; only one Python thread executes at once (though I/O calls can release the lock). This means multi-threaded Python code gives “little performance benefit” for CPU-heavy tasks. Python programmers do get concurrency by using multiprocessing (separate processes) or by doing asynchronous I/O (asyncio) for I/O-bound tasks. In summary, Python’s strength in concurrency lies in I/O concurrency (where threads sleep and release GIL) and ease of using processes for parallelism, but raw multithreading for compute speed is bottlenecked by the GIL.
• C++: C++ offers true multithreading with virtually no built-in locks preventing parallelism. Threads in C++ (e.g. std::thread) map directly to OS threads, so multiple threads can run on multiple CPU cores simultaneously without a GIL. This lets C++ programs achieve linear speedups for CPU-bound tasks if coded correctly. The trade-offs are that C++ puts more responsibility on the programmer: you must manage thread creation, synchronization (mutexes, atomics) and avoid data races yourself. But because C++ is “closer to the OS”, it can be slightly more efficient at thread context switching and memory access. In general, for concurrency C++ has the potential to be very fast (as fast as the hardware allows), and it supports many low-level optimizations (lock-free structures, CPU instructions for atomic ops, etc.).
• Java: Java also has built-in multithreading and concurrency libraries. Threads in Java map to native threads, so Java can use multiple cores (the OS schedules Java threads on cores). Java’s threading model includes features like synchronized blocks and the java.util.concurrent utilities (thread pools, atomic variables, locks). The JVM runtime can even optimize some threading patterns (e.g. escape analysis to remove locks where safe). In practice, Java’s multithreading is “high-performance”, and many large-scale server applications use it heavily. Compared to C++, Java threads have a bit more overhead (due to the JVM layer and garbage collection), but the JVM also does a lot of optimization work behind the scenes. One analysis notes that “Java’s runtime can optimize naive multi-threaded code better out of the box”, making it easier to get good concurrency performance without as much manual tuning. The downside is occasional GC pauses, but modern JVMs minimize this. Overall, Java offers robust concurrency with performance that is often slightly behind a tuned C++ solution, but it excels at productivity and safety (no dangling pointers, etc.) in multithreaded code.
• Julia: Julia has no GIL, so its concurrency model is different. For asynchronous tasks and I/O, Julia uses lightweight Tasks (coroutines) that can run cooperatively. For true parallel CPU execution, Julia introduced native multi-threading (with Threads.@threads, @spawn, etc.) starting in version 1.3. Now Julia can schedule multiple Tasks on multiple threads/cores: “Julia’s multi-threading provides the ability to schedule Tasks simultaneously on more than one thread or CPU core, sharing memory”. In practice, this means Julia can parallelize loops and computations across cores (similar to C++/Java threads), as long as you enable multiple threads at startup. Julia’s tasks are high-level, and since there’s no GIL, they can utilize all cores for CPU-bound work. However, Julia’s concurrency ecosystem is younger: developers must explicitly use the threading macros or distributed computing features. Julia is designed so that parallel constructs are composable (one threaded function can call another and Julia manages resources). For I/O concurrency, Julia’s tasks work like Python’s asyncio (a task yields control during blocking calls). In summary, Julia’s concurrency performance can approach C++/Java for parallel CPU tasks, given proper use of its threading features, and it avoids Python’s GIL limitation. Its high-level syntax and multiple-dispatch model make writing parallel loops relatively straightforward, but the language is still maturing in this area.

Conclusion

In conclusion, C++ generally offers the highest raw performance across numeric work, I/O, and concurrency, thanks to its compiled nature and minimal runtime overhead. Java usually comes next: it can be very fast (even rivaling C++ in some long-running tasks) because of JIT optimizations, though garbage collection and JVM overhead can hold it back slightly. Julia was built for high performance in numerics and has no interpreter lock, so it often outperforms Python and can even match C++ for heavy compute, while providing high-level syntax. Python, on the other hand, is the easiest to write but the slowest to run for these tasks (unless it uses optimized libraries). Python pays the price of an interpreter and dynamic typing, and its GIL prevents CPU threads from running in true parallel. The strengths and weaknesses align with each language’s design goals: C++ maximizes speed, Java balances speed with safety, Julia aims for speed in scientific code, and Python prioritizes programmer ease at the cost of speed. Understanding these architectural differences helps developers choose the right language or optimize code – for example, using C++ or Julia for number-crunching, Java for scalable services, and Python when rapid development is more important than raw speed.