Mobile Development: Performance Optimizations

Performance Optimizations for Mobile

Mobile devices have significantly different hardware constraints compared to desktop systems. In this section, we’ll explore key performance optimizations that are essential for achieving good performance on mobile platforms.

This chapter covers general mobile performance. For practices that arise specifically because the GPU is tile-based (TBR), see Rendering Approaches: Tile-Based Rendering.

Texture Optimizations

We focus on mobile‑specific decisions here. For general Vulkan image creation, staging uploads, and descriptor setup, refer back to earlier chapters in the engine series—Resource Management, Rendering Pipeline—or the Vulkan Guide (https://docs.vulkan.org/guide/latest/).

Textures are often the largest consumers of memory in a graphics application. Optimizing them is crucial for performance on both mobile and desktop.

Efficient Texture Formats

Choosing the right texture format is crucial across platforms; what differs is which formats are natively supported by a given device/driver:

  1. Compressed Formats: Use hardware-supported compressed formats whenever possible:

    • ASTC (Adaptive Scalable Texture Compression): Widely supported on modern mobile GPUs and increasingly available on desktop; excellent quality-to-size ratio with flexible block sizes.

    • ETC2/EAC: Required for OpenGL ES 3.0 and supported by most Android devices; commonly available on Vulkan stacks, too.

    • PVRTC: Primarily supported on iOS devices with PowerVR GPUs.

    • BC (Block Compression, a.k.a. DXT/BCn): Ubiquitous on desktop; supported by some mobile GPUs.

  2. Format Selection Based on Content and Support: Choose formats based on the type of texture and what the device reports:

    • For high detail (normals, roughness): prefer ASTC 4x4 or 6x6 when supported; on desktop, BC5/BC7 are common alternatives.

    • For albedo/basecolor: ASTC 6x6–8x8 works well when available; on desktop, BC1/BC7 are typical.

    • For single-channel data: consider R8 or compressed single-channel alternatives when available.

This guidance is not mobile-only: block compression reduces memory footprint and bandwidth on all platforms. The Mobile chapter highlights it because bandwidth and power are tighter constraints on phones/tablets. On desktop, the same benefits apply; the primary difference is which formats are commonly available (e.g., BC on desktop, ASTC/ETC2 on many mobile devices).

Here’s how to check for and use compressed formats in Vulkan:

bool is_format_supported(vk::PhysicalDevice physical_device, vk::Format format, vk::ImageTiling tiling,
                        vk::FormatFeatureFlags features) {
    vk::FormatProperties props = physical_device.getFormatProperties(format);

    if (tiling == vk::ImageTiling::eLinear) {
        return (props.linearTilingFeatures & features) == features;
    } else if (tiling == vk::ImageTiling::eOptimal) {
        return (props.optimalTilingFeatures & features) == features;
    }

    return false;
}

vk::Format find_supported_format(vk::PhysicalDevice physical_device,
                               const std::vector<vk::Format>& candidates,
                               vk::ImageTiling tiling,
                               vk::FormatFeatureFlags features) {
    for (vk::Format format : candidates) {
        if (is_format_supported(physical_device, format, tiling, features)) {
            return format;
        }
    }

    throw std::runtime_error("Failed to find supported format");
}

Memory Optimizations

Memory is a precious resource on all platforms. It tends to be more performance‑critical on mobile due to tighter bandwidth, power, and thermal budgets. Here are some key optimizations:

Minimize Memory Allocations

  1. Pool Allocations: Use memory pools to reduce the overhead of frequent allocations and deallocations.

  2. Suballocate from Larger Blocks: Instead of creating many small Vulkan memory allocations, allocate larger blocks and suballocate from them:

class VulkanMemoryPool {
public:
    VulkanMemoryPool(vk::Device device, vk::PhysicalDevice physical_device,
                    vk::DeviceSize block_size, uint32_t memory_type_index)
        : device(device), block_size(block_size), memory_type_index(memory_type_index) {
        allocate_new_block();
    }

    ~VulkanMemoryPool() {
        for (auto& block : memory_blocks) {
            device.freeMemory(block.memory);
        }
    }

    struct Allocation {
        vk::DeviceMemory memory;
        vk::DeviceSize offset;
        vk::DeviceSize size;
    };

    Allocation allocate(vk::DeviceSize size, vk::DeviceSize alignment) {
        // Find a block with enough space
        for (auto& block : memory_blocks) {
            vk::DeviceSize aligned_offset = align(block.next_offset, alignment);
            if (aligned_offset + size <= block_size) {
                Allocation alloc;
                alloc.memory = block.memory;
                alloc.offset = aligned_offset;
                alloc.size = size;

                block.next_offset = aligned_offset + size;
                return alloc;
            }
        }

        // No block has enough space, allocate a new one
        allocate_new_block();
        return allocate(size, alignment);  // Try again with the new block
    }

private:
    struct MemoryBlock {
        vk::DeviceMemory memory;
        vk::DeviceSize next_offset = 0;
    };

    void allocate_new_block() {
        vk::MemoryAllocateInfo alloc_info;
        alloc_info.setAllocationSize(block_size);
        alloc_info.setMemoryTypeIndex(memory_type_index);

        MemoryBlock block;
        block.memory = device.allocateMemory(alloc_info);
        block.next_offset = 0;

        memory_blocks.push_back(block);
    }

    vk::DeviceSize align(vk::DeviceSize offset, vk::DeviceSize alignment) {
        return (offset + alignment - 1) & ~(alignment - 1);
    }

    vk::Device device;
    vk::DeviceSize block_size;
    uint32_t memory_type_index;
    std::vector<MemoryBlock> memory_blocks;
};

Reduce Bandwidth Usage

  1. Minimize State Changes: Group draw calls by material to reduce state changes.

  2. Use Smaller Data Types: Use 16-bit indices and half-precision floats where appropriate.

  3. Optimize Vertex Formats: Use packed vertex formats to reduce memory bandwidth:

// Traditional vertex format (48 bytes per vertex)
struct Vertex {
    glm::vec3 position;   // 12 bytes
    glm::vec3 normal;     // 12 bytes
    glm::vec2 texCoord;   // 8 bytes
    glm::vec4 color;      // 16 bytes
};

// Optimized vertex format (16 bytes per vertex)
struct OptimizedVertex {
    // Position: 3 components, 16-bit float each
    uint16_t position[3]; // 6 bytes

    // Normal: 2 components (can reconstruct Z), 8-bit signed normalized
    int8_t normal[2];     // 2 bytes

    // TexCoord: 2 components, 16-bit float each
    uint16_t texCoord[2]; // 4 bytes

    // Color: 4 components, 8-bit unsigned normalized
    uint8_t color[4];     // 4 bytes
};

If you are targeting tile-based GPUs (TBR), bandwidth can be heavily impacted by attachment load/store behavior and tile flushes. See Rendering Approaches — sections “Attachment Load/Store Operations on Tilers” and “Pipelining on Tilers: Subpass Dependencies and BY_REGION” for concrete guidance.

Draw Call Optimizations

Mobile GPUs are particularly sensitive to draw call overhead:

  1. Instancing: Use instancing to reduce draw calls for repeated objects.

  2. Batching: Combine multiple objects into a single mesh where possible.

  3. Level of Detail (LOD): Implement LOD systems to reduce geometry complexity for distant objects.

On tile-based GPUs, reducing CPU overhead is important, but keeping work and data on-chip via proper pipelining and subpasses often yields larger gains. See Rendering Approaches — “Pipelining on Tilers: Subpass Dependencies and BY_REGION” for barrier/subpass patterns, and “Attachment Load/Store Operations on Tilers” for loadOp/storeOp guidance that avoids external memory traffic.

Vendor-Specific Optimizations

Different mobile GPU vendors have specific architectures that may benefit from targeted optimizations.

Vendor-Specific GPU Optimizations

Different mobile GPU vendors have specific architectures that benefit from targeted optimizations:

  • Memory Management: Many mobile SoCs have unified memory architecture:

    • Use VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT | VK_MEMORY_PROPERTY_HOST_VISIBLE_BIT memory when possible

    • Take advantage of fast CPU-GPU memory transfers in unified memory architectures

  • Texture Compression: Different devices support different texture compression formats:

// Check for texture compression format support
bool supports_texture_format(vk::PhysicalDevice physical_device, vk::Format format) {
    vk::FormatProperties props = physical_device.getFormatProperties(format);
    return (props.optimalTilingFeatures & vk::FormatFeatureFlagBits::eSampledImage);
}

// Get optimal texture format based on device capabilities
vk::Format get_optimal_texture_format(vk::PhysicalDevice physical_device) {
    vk::PhysicalDeviceProperties props = physical_device.getProperties();
    vk::PhysicalDeviceFeatures features = physical_device.getFeatures();

    // Check for ASTC support (widely supported on modern mobile GPUs)
    // Most games are written with knowledge of what the assets were compressed with so it's standard practice to only ensure the required format is supported.
    if (features.textureCompressionASTC_LDR) {
        return vk::Format::eAstc8x8SrgbBlock;
    }
}

  • Performance Monitoring: Most vendors provide performance monitoring tools that can help identify bottlenecks specific to their hardware.

Best Practices for Mobile Performance

  1. Profile on Target Devices: Performance characteristics vary widely across mobile devices. Test on a range of hardware from different manufacturers and with different GPU architectures.

  2. Monitor Temperature: Mobile devices throttle performance when they get hot. Design your engine to adapt to thermal throttling.

  3. Balance Quality and Performance: Provide graphics settings that allow users to balance quality and performance based on their device capabilities.

  4. Implement Adaptive Resolution: Dynamically adjust rendering resolution based on performance metrics.

In the next section, we’ll explore different rendering approaches for mobile GPUs, focusing on the differences between Tile-Based Rendering (TBR) and Immediate Mode Rendering (IMR).

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