KvikIO Reference — High-Performance GPU File IO

KvikIO is a Python and C++ library for high-performance file IO. It provides bindings to NVIDIA cuFile, enabling GPUDirect Storage (GDS) — reading and writing data directly between storage and GPU memory, bypassing CPU memory entirely. When GDS isn't available, KvikIO falls back gracefully to POSIX IO while still handling both host and device data seamlessly.

KvikIO is part of the RAPIDS ecosystem and interoperates with CuPy, cuDF, Numba, and other GPU libraries.

Table of Contents

1. Installation
2. When to Use KvikIO
3. CuFile — Local File IO
4. RemoteFile — S3, HTTP, WebHDFS
5. Zarr Integration
6. Memory-Mapped Files
7. Runtime Settings
8. Performance Optimization
9. Interoperability
10. Common Patterns
11. Common Pitfalls

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Installation

# CUDA 12.x
uv add kvikio-cu12

# CUDA 13.x
uv add kvikio-cu13

# For Zarr support (optional)
uv add zarr

Verify installation:

# Check if GDS is available

print(kvikio.cufile_driver.get("is_gds_available"))  # True if GDS is set up

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When to Use KvikIO

Use KvikIO when:

• Loading large binary data directly to GPU — avoids the CPU-memory copy that standard open() or NumPy's fromfile() would require
• Writing GPU arrays to disk — saves directly from device memory without copying to host first
• Reading from remote storage (S3, HTTP, WebHDFS) into GPU memory — skips the host-memory staging step
• Working with Zarr arrays on GPU — the GDSStore backend reads chunks directly into CuPy arrays
• IO is the bottleneck — GDS can achieve close to raw NVMe bandwidth (6-7 GB/s per drive) vs standard IO that tops out at CPU-memory bandwidth
• Overlapping IO and compute — non-blocking reads/writes let you pipeline data loading with GPU computation

KvikIO is a poor fit when:

• Data is small (< 1 MB) — kernel launch and GDS overhead dominate
• You're reading structured formats (CSV, Parquet, JSON) — use cuDF instead, which has its own optimized readers
• You only need host memory — standard Python IO is simpler

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CuFile — Local File IO

kvikio.CuFile is the primary interface for local file IO. It replaces Python's open() for GPU workloads.

Basic Usage

# Write a GPU array to disk
a = cp.arange(1_000_000, dtype=cp.float32)
with kvikio.CuFile("data.bin", "w") as f:
    f.write(a)

# Read it back
b = cp.empty(1_000_000, dtype=cp.float32)
with kvikio.CuFile("data.bin", "r") as f:
    f.read(b)

assert cp.all(a == b)

API Methods

File modes: "r" (read), "w" (write/truncate), "a" (append), "+" (read+write).

Non-Blocking IO with Futures

pread and pwrite split the operation into tasks executed in a thread pool and return an IOFuture:

data = cp.empty(10_000_000, dtype=cp.float32)

with kvikio.CuFile("data.bin", "r") as f:
    # Launch two non-blocking reads for different sections
    future1 = f.pread(data[:5_000_000])
    future2 = f.pread(data[5_000_000:], file_offset=5_000_000 * 4)

    # Do other work while IO happens...

    # Wait for completion
    bytes_read1 = future1.get()
    bytes_read2 = future2.get()

Partial Reads and Writes

# Read only a portion of a file
buf = cp.empty(1000, dtype=cp.float32)
with kvikio.CuFile("data.bin", "r") as f:
    # Read 1000 floats starting at byte offset 4000
    f.read(buf, size=4000, file_offset=4000)

Host Memory Support

KvikIO handles host memory transparently — no special API needed:

# Write from host memory
a = np.arange(1_000_000, dtype=np.float32)
with kvikio.CuFile("data.bin", "w") as f:
    f.write(a)

# Read into host memory
b = np.empty_like(a)
with kvikio.CuFile("data.bin", "r") as f:
    f.read(b)

GDS Alignment

GDS works best with page-aligned IO. The GPU page size is 4 KiB (4096 bytes):

• File offset: should be a multiple of 4096
• Transfer size: should be a multiple of 4096

KvikIO handles unaligned IO correctly but splits it into aligned and unaligned parts, so aligned IO will be faster.

---

RemoteFile — S3, HTTP, WebHDFS

kvikio.RemoteFile reads remote files directly into GPU or host memory.

HTTP/HTTPS

buf = cp.empty(1_000_000, dtype=cp.float32)
with kvikio.RemoteFile.open_http("https://example.com/data.bin") as f:
    print(f.nbytes())  # File size
    f.read(buf)

AWS S3

# Using bucket + object name (requires AWS env vars or explicit credentials)
with kvikio.RemoteFile.open_s3("my-bucket", "data/file.bin") as f:
    buf = cp.empty(f.nbytes(), dtype=cp.uint8)
    f.read(buf)

# Using S3 URL
with kvikio.RemoteFile.open_s3_url("s3://my-bucket/data/file.bin") as f:
    buf = cp.empty(f.nbytes(), dtype=cp.uint8)
    f.read(buf)

# Public S3 (no credentials needed)
with kvikio.RemoteFile.open_s3_public("s3://public-bucket/data.bin") as f:
    buf = cp.empty(f.nbytes(), dtype=cp.uint8)
    f.read(buf)

# Presigned URL
with kvikio.RemoteFile.open_s3_presigned_url(presigned_url) as f:
    buf = cp.empty(f.nbytes(), dtype=cp.uint8)
    f.read(buf)

AWS credentials come from environment variables (AWS_DEFAULT_REGION, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) or can be passed as keyword arguments.

Auto-Detect Endpoint Type

# KvikIO figures out the protocol from the URL
with kvikio.RemoteFile.open("s3://bucket/object") as f:
    ...

with kvikio.RemoteFile.open("https://example.com/file.bin") as f:
    ...

WebHDFS

with kvikio.RemoteFile.open_webhdfs("http://namenode:9870/path/to/file") as f:
    buf = cp.empty(f.nbytes(), dtype=cp.uint8)
    f.read(buf)

Host Memory with RemoteFile

RemoteFile reads into host memory just as easily:

with kvikio.RemoteFile.open_http("https://example.com/data.bin") as f:
    buf = np.empty(f.nbytes(), dtype=np.uint8)
    f.read(buf)

---

Zarr Integration

KvikIO provides a GPU store backend for Zarr (version 3.x). This enables reading and writing chunked N-dimensional arrays directly in GPU memory via GDS.

from kvikio.zarr import GDSStore

# Enable GPU support in Zarr
zarr.config.enable_gpu()

# Create a GDS-backed store
store = GDSStore(root="data.zarr")

# Create and write a Zarr array (data stays on GPU)
z = zarr.create_array(
    store=store,
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype="float32",
    overwrite=True,
)

# Reading returns CuPy arrays
chunk = z[:100, :100]  # Returns cupy.ndarray

Zarr + KvikIO is useful for:

• Climate/weather data (large multi-dimensional arrays)
• Bioinformatics (genomic arrays)
• Any workload using chunked arrays that need GPU processing

Requires: uv add zarr in addition to kvikio.

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Memory-Mapped Files

kvikio.mmap.Mmap provides memory-mapped file access with support for both host and device destinations:

from kvikio.mmap import Mmap

# Map a file for reading
with Mmap("data.bin", flags="r") as m:
    print(m.file_size())

    # Sequential read into device memory
    buf = cp.empty(1000, dtype=cp.float32)
    m.read(buf, size=4000, offset=0)

    # Parallel read (returns IOFuture)
    future = m.pread(buf, size=4000, offset=0)
    future.get()

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Runtime Settings

KvikIO behavior is controlled via environment variables or the kvikio.defaults API.

Key Settings

Programmatic Configuration

# Query settings
print(kvikio.defaults.get("compat_mode"))
print(kvikio.defaults.get("num_threads"))

# Modify settings at runtime
kvikio.defaults.set({"num_threads": 16, "task_size": 8 * 1024 * 1024})

# Enable direct IO for reads
kvikio.defaults.set({"auto_direct_io_read": True})

Compatibility Mode

When GDS isn't available (missing libcufile.so, running in WSL, Docker without /run/udev), AUTO mode falls back to POSIX IO automatically. This means KvikIO code works everywhere — it just runs faster when GDS is available.

# Check if GDS is actually being used
print(kvikio.cufile_driver.get("is_gds_available"))

cuFile Driver Configuration

# Query driver properties
print(kvikio.cufile_driver.get("is_gds_available"))
print(kvikio.cufile_driver.get("major_version"))

# Configure settable properties
kvikio.cufile_driver.set("max_device_cache_size", 1024)

# Use as context manager (auto-reverts on exit)
with kvikio.cufile_driver.set({"poll_mode": True}):
    # poll mode active here
    ...
# poll mode reverted

---

Performance Optimization

1. Increase Thread Pool Size

The default of 1 thread is conservative. For large files, increase it:

kvikio.defaults.set({"num_threads": 16})

2. Use Non-Blocking IO for Pipelining

Overlap IO with compute by using pread/pwrite:

# Pipeline: read chunk N while processing chunk N-1
chunk_size = 10_000_000
buf_a = cp.empty(chunk_size, dtype=cp.float32)
buf_b = cp.empty(chunk_size, dtype=cp.float32)

with kvikio.CuFile("large_data.bin", "r") as f:
    # Start first read
    future = f.pread(buf_a)
    future.get()

    for offset in range(chunk_size * 4, file_size, chunk_size * 4):
        # Start next read while processing current
        next_future = f.pread(buf_b, file_offset=offset)

        # Process buf_a on GPU (overlaps with IO)
        result = cp.fft.fft(buf_a)

        next_future.get()
        buf_a, buf_b = buf_b, buf_a  # Swap buffers

3. Align IO to Page Boundaries

GDS performs best with 4 KiB-aligned offsets and sizes:

# Good: aligned offset and size
f.read(buf, size=4096 * 1000, file_offset=4096 * 10)

# Slower: unaligned (KvikIO handles it, but splits into aligned + unaligned parts)
f.read(buf, size=5000, file_offset=100)

4. Enable Direct IO

For sequential writes and cold reads, Direct IO (bypassing OS page cache) can help:

kvikio.defaults.set({
    "auto_direct_io_read": True,
    "auto_direct_io_write": True,
})

5. Tune Task and Bounce Buffer Sizes

For very large files, increase task and bounce buffer sizes:

kvikio.defaults.set({
    "task_size": 16 * 1024 * 1024,       # 16 MiB per task
    "bounce_buffer_size": 64 * 1024 * 1024,  # 64 MiB bounce buffer
})

6. Page Cache Utilities

For benchmarking, clear the page cache to measure cold-read performance:

# Check page cache residency
pages_cached, total_pages = kvikio.get_page_cache_info("data.bin")
print(f"{pages_cached}/{total_pages} pages in cache")

# Clear page cache (requires root or appropriate permissions)
kvikio.clear_page_cache()

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Interoperability

With CuPy

KvikIO reads directly into CuPy arrays — this is the most common usage:

data = cp.empty(1_000_000, dtype=cp.float64)
with kvikio.CuFile("data.bin", "r") as f:
    f.read(data)
# data is now a CuPy array, ready for GPU computation

With Numba CUDA

KvikIO works with any buffer supporting the CUDA Array Interface:

from numba import cuda

d_arr = cuda.device_array(1_000_000, dtype="float32")
with kvikio.CuFile("data.bin", "r") as f:
    f.read(d_arr)

With cuDF

For raw binary data that isn't in a tabular format, use KvikIO to load, then convert:

# Load raw float array, wrap as cuDF Series
buf = cp.empty(1_000_000, dtype=cp.float32)
with kvikio.CuFile("signal.bin", "r") as f:
    f.read(buf)
signal = cudf.Series(buf)

For tabular formats (CSV, Parquet, JSON, ORC), use cuDF's own readers — they're optimized for those formats.

With NumPy (Host Memory)

KvikIO seamlessly handles host memory:

arr = np.empty(1_000_000, dtype=np.float32)
with kvikio.CuFile("data.bin", "r") as f:
    f.read(arr)

---

Common Patterns

Save and Load GPU Model Checkpoints

def save_checkpoint(arrays: dict[str, cp.ndarray], path: str):
    """Save multiple GPU arrays to a single file."""
    with kvikio.CuFile(path, "w") as f:
        offset = 0
        for arr in arrays.values():
            f.write(arr, file_offset=offset)
            offset += arr.nbytes

def load_checkpoint(shapes_dtypes: dict, path: str) -> dict[str, cp.ndarray]:
    """Load GPU arrays from a checkpoint file."""
    arrays = {}
    with kvikio.CuFile(path, "r") as f:
        offset = 0
        for name, (shape, dtype) in shapes_dtypes.items():
            arr = cp.empty(shape, dtype=dtype)
            f.read(arr, file_offset=offset)
            offset += arr.nbytes
            arrays[name] = arr
    return arrays

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